%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65028 %T Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks %A Etholén,Antti %A Roos,Teemu %A Hänninen,Mirja %A Bouri,Ioanna %A Kulmala,Jenni %A Rahkonen,Ossi %A Kouvonen,Anne %A Lallukka,Tea %+ Department of Public Health, University of Helsinki, PO Box 20, Tukholmankatu 8 B, Helsinki, 00014, Finland, 358 445105010, antti.etholen@helsinki.fi %K artificial intelligence %K AI %K dementia %K aging %K smoking %K alcohol consumption %K leisure time physical activity %K consumption of fruit and vegetables %K body mass index %K BMI %K insomnia symptoms %D 2025 %7 6.5.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention of cognitive decline are needed. Objective: We examined multifactorial trajectories of risk factors and their associations with SCD using an artificial intelligence (AI) approach to build a score calculator that forecasts later SCD. In addition, we aimed to develop a new risk score tool to facilitate personalized risk assessment and intervention planning and to validate SCD against register-based dementia diagnoses and dementia-related medications. Methods: Five repeated surveys (2000-2022) of the Helsinki Health Study (N=8960; n=7168, 80% women, aged 40-60 years in phase 1) were used to build dynamic Bayesian networks for estimating the odds of SCD. The model structure was developed using expert knowledge and automated techniques, implementing a score-based approach for training dynamic Bayesian networks with the quotient normalized maximum likelihood criterion. The developed model was used to predict SCD (memory, learning, and concentration) based on the history of consumption of fruit and vegetables, smoking, alcohol consumption, leisure time physical activity, BMI, and insomnia symptoms, adjusting for sociodemographic covariates. Model performance was assessed using 5-fold cross-validation to calculate the area under the receiver operating characteristic curve. Bayesian credible intervals were used to quantify uncertainty in model estimates. Results: Of the participants, 1842 of 5865 (31%) reported a decline in memory, 2818 of 5879 (47.4%) in learning abilities, and 1828 of 5888 (30.7%) in concentration in 2022. Physical activity was the strongest predictor of SCD in a 5-year interval, with an odds ratio of 0.76 (95% Bayesian credible interval 0.59-0.99) for physically active compared to inactive participants. Alcohol consumption showed a U-shaped relationship with SCD. Other risk factors had minor effects. Moreover, our validation confirmed that SCD has prognostic value for diagnosed dementia, with individuals reporting memory decline being over 3 times more likely to have dementia in 2017 (age 57-77 years), and this risk increased to more than 5 times by 2022 (age 62-82 years). The receiver operating characteristic curve analysis further supported the predictive validity of our outcome, with an area under the curve of 0.78 in 2017 and 0.75 in 2022. Conclusions: A new risk score tool was developed that enables individuals to inspect their risk profiles and explore potential targets for interventions and their estimated contributions to later SCD. Using AI-driven predictive modeling, the tool can aid health care professionals in providing personalized prevention strategies. A dynamic decision heatmap was presented as a communication tool to be used at health care consultations. Our findings suggest that early identification of individuals with SCD could improve targeted intervention strategies for reducing dementia risk. Future research should explore the integration of AI-based risk prediction models into clinical workflows and assess their effectiveness in guiding lifestyle interventions to mitigate SCD and dementia. %M 40327854 %R 10.2196/65028 %U https://www.jmir.org/2025/1/e65028 %U https://doi.org/10.2196/65028 %U http://www.ncbi.nlm.nih.gov/pubmed/40327854 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64228 %T Trust and Privacy Concerns Among Cancer Survivors Who Did Not Visit a Research Website Offering Free Genetic Counseling Services for Families: Survey Study %A Shepperd,James A %A McBride,Colleen M %A An,Weihua %A Zhao,Jingsong %A Pentz,Rebecca D %A Escoffery,Cam %A Ward,Kevin %A Guan,Yue %+ Department of Psychology, University of Florida, 945 Center Drive, Gainesville, FL, 32611, United States, 1 3522732165, shepperd@ufl.edu %K internet trust %K internet privacy %K hereditary cancers %K patients and relatives outreach %K social marketing %D 2025 %7 6.5.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Digital health tools, such as websites, now proliferate to assist individuals in managing their health. With user input, we developed the Your Family Connects (YFC) website to promote access to genetic services for survivors of ovarian cancer and their relatives. Although we estimated that half or more would access the website, only 18% of invited survivors did so. We assessed the extent to which perceived relevance of the information provided, trust, and privacy concerns influenced decisions not to access the website. Objective: We designed a theory-based cross-sectional survey to explore the following questions: (1) To what extent did nonresponders endorse privacy concerns? (2) Were privacy concerns associated with recall of receiving the website invitation, time since diagnosis, age, and race? (3) Could we identify profiles of nonresponders that would guide the development of future interventions to encourage engagement in health websites for families affected by inherited cancers? Methods: A sample of survivors who were eligible to access the website yet did not respond to the study invitation was identified by linking study IDs to the Georgia Cancer Registry information. The survey was brief and contained 27 items, including recall of the invitation, interest in ovarian cancer information, benefits of using health websites, trust in health websites, and trust in university-based health research. We conducted factor analyses, regression analyses, ANOVA, correlation analyses, and logistic regression to address research questions. Results: Of the 650 nonresponders to whom we sent the short survey, 368 (56.3%) responded and provided sufficient data for analysis. The mean response of 2.57 on the trust scale was significantly below the scale midpoint of 3 (t360=11.78, P<.001), suggesting that survivors who did not log on were on average distrustful of health websites. Belonging to a racial or ethnic minority group was associated with being more trusting and less skeptical about health websites. Just 196 (30.1%) nonresponders recalled the invitation to visit the website. Logistic regression analysis indicated that age was the only significant predictor of recall. Testing a model with age, racial or ethnic minority status, and the 6 privacy concerns correctly classified 58.8% of nonresponders, a rate of successful classification that was not appreciably better than a logistic regression analysis that included only age as a predictor. Conclusions: The nonresponders in the present study—particularly the White nonresponders—were skeptical of website platforms regardless of whether they recalled receiving a website invitation or not. Social marketing approaches geared toward building trust in web platforms by building a relationship with an information consumer and in collaboration with trusted organizations warrant further investigation. Trial Registration: ClinicalTrials.gov NCT04927013; https://clinicaltrials.gov/study/NCT04927013 %M 40327861 %R 10.2196/64228 %U https://www.jmir.org/2025/1/e64228 %U https://doi.org/10.2196/64228 %U http://www.ncbi.nlm.nih.gov/pubmed/40327861 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 9 %N %P e71016 %T The rs243865 Polymorphism in Matrix Metalloproteinase-2 and its Association With Target Organ Damage in Patients With Resistant Hypertension: Cross-Sectional Study %A Tuan Huynh,An %A Vu,Hoang Anh %A Chuong,Ho Quoc %A Anh,Tien Hoang %A Viet Tran,An %K resistant hypertension %K matrix metalloproteinase-2 %K gene polymorphism %K target organ damage %K arterial stiffness %D 2025 %7 1.5.2025 %9 %J JMIR Cardio %G English %X Background: Resistant hypertension (RH) presents significant clinical challenges, often precipitating a spectrum of cardiovascular complications. Particular attention recently has focused on the role of matrix metalloproteinase-2 (MMP-2) gene polymorphisms, implicated in hypertensive target organ damage (TOD). Despite growing interest, the specific contribution of MMP-2 polymorphisms to such damage in RH remains inadequately defined. Objective: This study is the first to examine the rs243865 (−1306C>T) polymorphism in the MMP-2 gene in the Vietnamese population and patients with RH, underscoring its critical role as a genetic determinant of TOD. Methods: A cross-sectional study with both descriptive and analytical components was conducted with 78 patients with RH at the Can Tho Central General Hospital and Can Tho University of Medicine and Pharmacy Hospital from July 2023 to February 2024. Results: More than three-quarters of patients with RH had carotid-femoral pulse wave velocity (PWV) >10 m/s and microalbuminuria at a prevalence of 79% (62/78) and 76% (59/78), respectively, and more than half of patients with RH had left ventricular mass index, relative wall thickness, and carotid artery stenosis with a prevalence of 56% (45/78), 55% (43/78), and 53% (41/78), respectively. Of the 78 studied patients with RH, the presence of genotype CC was 77% (60/78), genotype CT accounted for 21% (16/78), and genotype TT for 3% (2/78). The presence of single nucleotide polymorphism rs243865 (−1306C>T) with allele T was 23% (18/78). The MMP-2 gene polymorphism 1306C/T (rs243865) was significantly associated with ejection fraction and carotid artery stenosis with odds ratios (ORs) 8.1 (95% CI 1.3‐51.4; P=.03) and 4.5 (95% CI 1.1‐20.1; P=.048), respectively. The allele T was found to be significantly associated with arterial stiffness including brachial-ankle PWV and carotid-femoral PWV with the correlation coefficient of OR 2.2 (95% CI 0.6‐3.8) and OR 1.8 (95% CI 0.5‐3.2), respectively. Conclusions: The MMP-2 gene polymorphism rs243865 (−1306C>T) may have an association with measurable TOD in RH. %R 10.2196/71016 %U https://cardio.jmir.org/2025/1/e71016 %U https://doi.org/10.2196/71016 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 11 %N %P e62756 %T Survival Tree Analysis of Interactions Among Factors Associated With Colorectal Cancer Risk in Patients With Type 2 Diabetes: Retrospective Cohort Study %A Yau,Sarah Tsz Yui %A Hung,Chi Tim %A Leung,Eman Yee Man %A Lee,Albert %A Yeoh,Eng Kiong %K colorectal cancer %K risk factor %K interaction %K type 2 diabetes %K survival analysis %K decision tree %K recursive partitioning %K segmentation %K risk stratification %K public health %D 2025 %7 29.4.2025 %9 %J JMIR Public Health Surveill %G English %X Background: Colorectal cancer (CRC) and diabetes share many common lifestyle risk factors, such as obesity. However, it remains largely unknown how different factors interact to influence the risk of CRC development among patients with diabetes. Objective: This study aimed to identify the interaction patterns among factors associated with the risk of CRC incidence among patients with diabetes. Methods: This is a retrospective cohort study conducted using electronic health records from Hong Kong. Patients who were diagnosed with type 2 diabetes and received care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. A conditional inference survival tree was applied to examine the interaction patterns among factors associated with the risk of CRC. Results: A total of 386,325 patients were included. During a median follow-up of 6.2 years (IQR 3.3-8.0), 4199 patients developed CRC. Patients were first partitioned into 4 age groups by increased levels of CRC risk (≤54 vs 55 to 61 vs 62 to 73 vs >73 years). Among patients aged more than 54 years, male sex was the dominant risk factor for CRC within each age stratum and the associations lessened with age. Abdominal obesity (waist-to-hip ratio >0.95) and longer duration of diabetes (median 12, IQR 7-18 vs median 4, IQR 1-11 years) were identified as key risk factor for CRC among men aged between 62 and 73 years and women aged more than 73 years, respectively. Conclusions: This study suggests the interaction patterns among age, sex, waist-to-hip ratio, and duration of diabetes on the risk of CRC incidence among patients with diabetes. Findings of the study may help identify target groups for public health intervention strategies. %R 10.2196/62756 %U https://publichealth.jmir.org/2025/1/e62756 %U https://doi.org/10.2196/62756 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e67298 %T Relationship Between Within-Session Digital Motor Skill Acquisition and Alzheimer Disease Risk Factors Among the MindCrowd Cohort: Cross-Sectional Descriptive Study %A Hooyman,Andrew %A Huentelman,Matt J %A De Both,Matt %A Ryan,Lee %A Duff,Kevin %A Schaefer,Sydney Y %K digital health technology %K web-based assessment %K aging %K APOE %K motor skills %K sensitivity %K risk factors %K adults %K older adults %D 2025 %7 24.4.2025 %9 %J JMIR Aging %G English %X Background: Previous research has shown that in-lab motor skill acquisition (supervised by an experimenter) is sensitive to biomarkers of Alzheimer disease (AD). However, remote unsupervised screening of AD risk through a skill-based task via the web has the potential to sample a wider and more diverse pool of individuals at scale. Objective: The purpose of this study was to examine a web-based motor skill game (“Super G”) and its sensitivity to risk factors of AD (eg, age, sex, APOE ε4 carrier status, and verbal learning deficits). Methods: Emails were sent to 662 previous MindCrowd participants who had agreed to be contacted for future research and have their APOE ε4 carrier status recorded and those who were at least 45 years of age or older. Participants who chose to participate were redirected to the Super G site where they completed the Super G task using their personal computer remotely and unsupervised. Once completed, different Super G variables were derived. Linear and logistic multivariable regression was used to examine the relationship between available AD risk factors (age, sex, APOE ε4 carrier status, and verbal learning) and distinct Super G performance metrics. Results: Fifty-four participants (~8% response rate) from the MindCrowd web-based cohort (mean age of 62.39 years; 39 females; and 23 APOE ε4 carriers) completed 75 trials of Super G. Results show that Super G performance was significantly associated with each of the targeted risk factors. Specifically, slower Super G response time was associated with being an APOE ε4 carrier (odds ratio 0.12, 95% CI 0.02-0.44; P=.006), greater Super G time in target (TinT) was associated with being male (odds ratio 32.03, 95% CI 3.74-1192,61; P=.01), and lower Super G TinT was associated with greater age (β −3.97, 95% CI −6.64 to −1.30; P=.005). Furthermore, a sex-by-TinT interaction demonstrated a differential relationship between Super G TinT and verbal learning depending on sex (βmale:TinT 6.77, 95% CI 0.34-13.19; P=.04). Conclusions: This experiment demonstrated that this web-based game, Super G, has the potential to be a skill-based digital biomarker for screening of AD risk on a large scale with relatively limited resources. %R 10.2196/67298 %U https://aging.jmir.org/2025/1/e67298 %U https://doi.org/10.2196/67298 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e71777 %T Advancing Emergency Care With Digital Twins %A Li,Haoran %A Zhang,Jingya %A Zhang,Ning %A Zhu,Bin %+ School of Public Health and Emergency Management, Southern University of Science and Technology, 1088 Xueyuan Avenue, Nanshan District, Shenzhen, 518000, China, 86 13530405020, zhub6@sustech.edu.cn %K emergency care %K digital twin %K prehospital emergency care %K in-hospital emergency care %K recovery %D 2025 %7 21.4.2025 %9 Viewpoint %J JMIR Aging %G English %X Digital twins—dynamic and real-time simulations of systems or environments—represent a paradigm shift in emergency medicine. We explore their applications across prehospital care, in-hospital management, and recovery. By integrating real-time data, wearable technology, and predictive analytics, digital twins hold the promise of optimizing resource allocation, advancing precision medicine, and tailoring rehabilitation strategies. Moreover, we discuss the challenges associated with their implementation, including data resolution, biological heterogeneity, and ethical considerations, emphasizing the need for actionable frameworks that balance innovation with data governance and public trust. %M 40258270 %R 10.2196/71777 %U https://aging.jmir.org/2025/1/e71777 %U https://doi.org/10.2196/71777 %U http://www.ncbi.nlm.nih.gov/pubmed/40258270 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e67922 %T AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis %A Xu,He-Li %A Li,Xiao-Ying %A Jia,Ming-Qian %A Ma,Qi-Peng %A Zhang,Ying-Hua %A Liu,Fang-Hua %A Qin,Ying %A Chen,Yu-Han %A Li,Yu %A Chen,Xi-Yang %A Xu,Yi-Lin %A Li,Dong-Run %A Wang,Dong-Dong %A Huang,Dong-Hui %A Xiao,Qian %A Zhao,Yu-Hong %A Gao,Song %A Qin,Xue %A Tao,Tao %A Gong,Ting-Ting %A Wu,Qi-Jun %+ Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36, San Hao Street, ShenYang, 110004, China, 86 024 96615 13652, wuqj@sj-hospital.org %K artificial intelligence %K AI %K blood biomarker %K ovarian cancer %K diagnosis %K PRISMA %D 2025 %7 24.3.2025 %9 Review %J J Med Internet Res %G English %X Background: Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent. Objective: We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis. Methods: A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies–AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis. Results: A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis. Conclusions: AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies. Trial Registration: PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232 %M 40126546 %R 10.2196/67922 %U https://www.jmir.org/2025/1/e67922 %U https://doi.org/10.2196/67922 %U http://www.ncbi.nlm.nih.gov/pubmed/40126546 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 6 %N %P e65001 %T A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study %A Huang,Tracy %A Ngan,Chun-Kit %A Cheung,Yin Ting %A Marcotte,Madelyn %A Cabrera,Benjamin %+ Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, United States, 1 (508) 831 5000, cngan@wpi.edu %K machine learning %K data driven %K clinical domain–guided framework %K survivors of cancer %K cancer %K oncology %K behavioral outcome predictions %K behavioral study %K behavioral outcomes %K feature selection %K deep learning %K neural network %K hybrid %K prediction %K predictive modeling %K patients with cancer %K deep learning models %K leukemia %K computational study %K computational biology %D 2025 %7 13.3.2025 %9 Original Paper %J JMIR Bioinform Biotech %G English %X Background: The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments. Objective: This study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer. Methods: We devised a hybrid deep learning–based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. Within a data-driven, clinical domain–guided framework to select the best set of features among cancer treatments, chronic health conditions, and socioenvironmental factors, we developed a 2-stage feature selection algorithm, that is, a multimetric, majority-voting filter and a deep dropout neural network, to dynamically and automatically select the best set of features for each behavioral outcome. We also conducted an experimental case study on existing study data with 102 survivors of acute lymphoblastic leukemia (aged 15-39 years at evaluation and >5 years postcancer diagnosis) who were treated in a public hospital in Hong Kong. Finally, we designed and implemented radial charts to illustrate the significance of the selected features on each behavioral outcome to support clinical professionals’ future treatment and diagnoses. Results: In this pilot study, we demonstrated that our approach outperforms the traditional statistical and computation methods, including linear and nonlinear feature selectors, for the addressed top-priority behavioral outcomes. Our approach holistically has higher F1, precision, and recall scores compared to existing feature selection methods. The models in this study select several significant clinical and socioenvironmental variables as risk factors associated with the development of behavioral problems in young survivors of acute lymphoblastic leukemia. Conclusions: Our novel feature selection algorithm has the potential to improve machine learning classifiers’ capability to predict adverse long-term behavioral outcomes in survivors of cancer. %M 40080820 %R 10.2196/65001 %U https://bioinform.jmir.org/2025/1/e65001 %U https://doi.org/10.2196/65001 %U http://www.ncbi.nlm.nih.gov/pubmed/40080820 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e44027 %T Novel Versus Conventional Sequencing of β-Blockers, Sodium/Glucose Cotransportor 2 Inhibitors, Angiotensin Receptor-Neprilysin Inhibitors, and Mineralocorticoid Receptor Antagonists in Stable Patients With Heart Failure With Reduced Ejection Fraction (NovCon Sequencing Study): Protocol for a Randomized Controlled Trial %A Karamchand,Sumanth %A Chipamaunga,Tsungai %A Naidoo,Poobalan %A Naidoo,Kiolan %A Rambiritch,Virendra %A Ho,Kevin %A Chilton,Robert %A McMahon,Kyle %A Leisegang,Rory %A Weich,Hellmuth %A Hassan,Karim %+ School of Law, University of South Africa, Preller Street, Pretoria, 2090, South Africa, 27 662698322, kiolan.naidoo@gmail.com %K heart failure %K SGLT2i %K sodium/glucose cotransporter 2 inhibitors %K ARNi %K angiotensin receptor-neprilysin inhibitors %K HFrEF %K heart failure with reduced ejection fraction %K idiopathic dilated cardiomyopathy %K heart %K chronic heart failure %K patient %K control %K clinical %K adult %K cardiomyopathy %K therapy %D 2025 %7 10.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Chronic heart failure has high morbidity and mortality, with approximately half of the patients dying within 5 years of diagnosis. Recent additions to the armamentarium of anti–heart failure therapies include angiotensin receptor-neprilysin inhibitors (ARNIs) and sodium/glucose cotransporter 2 inhibitors (SGLT2is). Both classes have demonstrated mortality and morbidity benefits. Although these new therapies have morbidity and mortality benefits, it is not known whether rapid initiation is beneficial when compared with the conventional, slower-stepped approach. Many clinicians have been taught that starting with low-dose therapies and gradually increasing the dose is a safe way of intensifying treatment regimens. Pharmacologically, it is rational to use a combination of drugs that target multiple pathological mechanisms, as there is potential synergism and better therapeutic outcomes. Theoretically, the quicker the right combinations are used, the more likely the beneficial effects will be experienced. However, rapid up-titration must be balanced with patient safety and tolerability. Objective: This study aims to determine if early addition of ARNIs, SGLT2is, β-blockers, and mineralocorticoid receptor antagonists (within 4 weeks), when compared with the same therapies initiated slower (within 6 months), will reduce all-cause mortality and hospitalizations for heart failure in patients with stable heart failure with reduced ejection fraction. Methods: This is a single-center, randomized controlled, double-arm, assessor-blinded, active control, and pragmatic clinical trial. Adults with stable heart failure with reduced ejection fraction and idiopathic dilated cardiomyopathy will be randomized to conventional sequencing (the control arm; over 6 months) of anti–heart failure therapies, and a second arm will receive rapid sequencing (over 4 weeks). Study participants will be followed for 5 years to assess the safety, efficacy, and tolerability of the 2 types of sequencing. Posttrial access and care will be provided to all study participants throughout their lifespan. Results: We are currently in the process of obtaining ethical clearance and funding. Conclusions: We envisage that this study will help support evidence-based medicine and inform clinical practice guidelines on the optimal rate of sequencing of anti–heart failure therapies. A third placebo arm was considered, but costs would be too much and not providing study participants with therapies with known morbidity and mortality benefits may be unethical, in our opinion. Given the post–COVID-19 economic downturn and posttrial access to interventions, a major challenge will be acquiring funding for this study. International Registered Report Identifier (IRRID): PRR1-10.2196/44027 %M 40063943 %R 10.2196/44027 %U https://www.researchprotocols.org/2025/1/e44027 %U https://doi.org/10.2196/44027 %U http://www.ncbi.nlm.nih.gov/pubmed/40063943 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e51975 %T FRAILSURVEY—an mHealth App for Self-Assessment of Frailty Based on the Portuguese Version of the Groningen Frailty Indicator: Validation and Reliability Study %A Midao,Luis %A Duarte,Mafalda %A Sampaio,Rute %A Almada,Marta %A Dias,Cláudia Camila %A Paúl,Constança %A Costa,Elísio %+ RISE-Health, Biochemistry Lab, Faculty of Pharmacy, University of Porto, R. Jorge de Viterbo Ferreira 228, Porto, 4050-313, Portugal, 351 22 042 8500, luismidao@gmail.com %K frailty %K mHealth %K assessment %K validation %K GFI %K reliability %K self-assessment %K Groningen Frailty Indicator %K FRAILSURVEY %K mobile phone %D 2025 %7 7.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Portugal is facing the challenge of population ageing, with a notable increase in the proportion of older individuals. This has positioned the country among those in Europe with a high prevalence of frailty. Frailty, a geriatric syndrome characterized by diminished physiological reserve and heightened vulnerability to stressors, imposes a substantial burden on public health. Objective: This study seeks to address two primary objectives: (1) translation and psychometric evaluation of the European Portuguese version of the Groningen Frailty Indicator (GFI); and (2) development and evaluation of the FRAILSURVEY app, a novel assessment tool for frailty based on the GFI. By achieving these objectives, the study aims to enhance the accuracy and reliability of frailty assessment in the Portuguese context, ultimately contributing to improved health care outcomes for older individuals in the region. Methods: To accomplish the objectives of the study, a comprehensive research methodology was used. The study comprised 2 major phases: the initial translation and validation of the GFI into European Portuguese and the development of the FRAILSURVEY app. Following this, an extensive examination of the app’s validity and reliability was conducted compared with the conventional paper version of the GFI. A randomized repeated crossover design was used to ensure rigorous evaluation of both assessment methods, using both the paper form of the GFI and the smartphone-based app FRAILSURVEY. Results: The findings of the study revealed promising outcomes in line with the research objectives. The meticulous translation process yielded a final version of the GFI with robust psychometric properties, ensuring clarity and comprehensibility for participants. The study included 522 participants, predominantly women (367/522, 70.3%), with a mean age of 73.7 (SD 6.7) years. Psychometric evaluation of the European Portuguese GFI in paper form demonstrates good reliability (internal consistency: Cronbach a value of 0.759; temporal stability: intraclass correlation coefficient=0.974) and construct validity (revealing a 4D structure explaining 56% of variance). Evaluation of the app-based European Portuguese GFI indicates good reliability (interinstrument reliability: Cohen k=0.790; temporal stability: intraclass correlation coefficient=0.800) and concurrent validity (r=0.694; P<.001). Conclusions: Both the smartphone-based app and the paper version of the GFI were feasible and acceptable for use. The findings supported that FRAILSURVEY exhibited comparable validity and reliability to its paper counterpart. FRAILSURVEY uses a standardized and validated assessment tool, offering objective and consistent measurements while eliminating subjective biases, enhancing accuracy, and ensuring reliability. This app holds promising potential for aiding health care professionals in identifying frailty in older individuals, enabling early intervention, and improving the management of adverse health outcomes associated with this syndrome. Its integration with electronic health records and other data may lead to personalized interventions, improving frailty management and health outcomes for at-risk individuals. %M 40053720 %R 10.2196/51975 %U https://formative.jmir.org/2025/1/e51975 %U https://doi.org/10.2196/51975 %U http://www.ncbi.nlm.nih.gov/pubmed/40053720 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e64364 %T Retrieval Augmented Therapy Suggestion for Molecular Tumor Boards: Algorithmic Development and Validation Study %A Berman,Eliza %A Sundberg Malek,Holly %A Bitzer,Michael %A Malek,Nisar %A Eickhoff,Carsten %+ Center for Digital Health, University Hospital Tuebingen, Schaffhausenstrasse 77, Tuebingen, 72072, Germany, 49 70712984350, eliza_berman@alumni.brown.edu %K large language models %K retrieval augmented generation %K LLaMA %K precision oncology %K molecular tumor board %K molecular tumor %K LLMs %K augmented therapy %K MTB %K oncology %K tumor %K clinical trials %K patient care %K treatment %K evidence-based %K accessibility to care %D 2025 %7 5.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Molecular tumor boards (MTBs) require intensive manual investigation to generate optimal treatment recommendations for patients. Large language models (LLMs) can catalyze MTB recommendations, decrease human error, improve accessibility to care, and enhance the efficiency of precision oncology. Objective: In this study, we aimed to investigate the efficacy of LLM-generated treatments for MTB patients. We specifically investigate the LLMs’ ability to generate evidence-based treatment recommendations using PubMed references. Methods: We built a retrieval augmented generation pipeline using PubMed data. We prompted the resulting LLM to generate treatment recommendations with PubMed references using a test set of patients from an MTB conference at a large comprehensive cancer center at a tertiary care institution. Members of the MTB manually assessed the relevancy and correctness of the generated responses. Results: A total of 75% of the referenced articles were properly cited from PubMed, while 17% of the referenced articles were hallucinations, and the remaining were not properly cited from PubMed. Clinician-generated LLM queries achieved higher accuracy through clinician evaluation than automated queries, with clinicians labeling 25% of LLM responses as equal to their recommendations and 37.5% as alternative plausible treatments. Conclusions: This study demonstrates how retrieval augmented generation–enhanced LLMs can be a powerful tool in accelerating MTB conferences, as LLMs are sometimes capable of achieving clinician-equal treatment recommendations. However, further investigation is required to achieve stable results with zero hallucinations. LLMs signify a scalable solution to the time-intensive process of MTB investigations. However, LLM performance demonstrates that they must be used with heavy clinician supervision, and cannot yet fully automate the MTB pipeline. %M 40053768 %R 10.2196/64364 %U https://www.jmir.org/2025/1/e64364 %U https://doi.org/10.2196/64364 %U http://www.ncbi.nlm.nih.gov/pubmed/40053768 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e69544 %T Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges %A Ringeval,Mickaël %A Etindele Sosso,Faustin Armel %A Cousineau,Martin %A Paré,Guy %+ HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 2A7, Canada, 1 5143406000, mickael.ringeval@hec.ca %K digital twins %K meta-review %K health IT %K applications %K challenges %K healthcare innovation %K personalized medicine %K operational efficiency %D 2025 %7 19.2.2025 %9 Review %J J Med Internet Res %G English %X Background: Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration of DTs in health care faces challenges such as fragmented applications, ethical concerns, and barriers to adoption. Objective: This study systematically reviews the existing literature on DT applications in health care with three objectives: (1) to map primary applications, (2) to identify key challenges and limitations, and (3) to highlight gaps that can guide future research. Methods: A meta-review was conducted in a systematic fashion, adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, and included 25 literature reviews published between 2021 and 2024. The search encompassed 5 databases: PubMed, CINAHL, Web of Science, Embase, and PsycINFO. Thematic synthesis was used to categorize DT applications, stakeholders, and barriers to adoption. Results: A total of 3 primary DT applications in health care were identified: personalized medicine, operational efficiency, and medical research. While current applications, such as predictive diagnostics, patient-specific treatment simulations, and hospital resource optimization, remain in their early stages of development, they highlight the significant potential of DTs. Challenges include data quality, ethical issues, and socioeconomic barriers. This review also identified gaps in scalability, interoperability, and clinical validation. Conclusions: DTs hold transformative potential in health care, providing individualized care, operational optimization, and accelerated research. However, their adoption is hindered by technical, ethical, and financial barriers. Addressing these issues requires interdisciplinary collaboration, standardized protocols, and inclusive implementation strategies to ensure equitable access and meaningful impact. %M 39969978 %R 10.2196/69544 %U https://www.jmir.org/2025/1/e69544 %U https://doi.org/10.2196/69544 %U http://www.ncbi.nlm.nih.gov/pubmed/39969978 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e70075 %T Histopathological Comparison and Expression Analysis of COL1A1, COL3A1, and ELN in the Proximal and Distal Ventral Dartos of Patients With Hypospadias: Protocol for Prospective Case-Control Study %A Raharja,Putu Angga Risky %A Birowo,Ponco %A Rachmadi,Lisnawati %A Wibowo,Heri %A Kekalih,Aria %A Duarsa,Gede Wirya Kusuma %A Abbas,Tariq %A Wahyudi,Irfan %+ Department of Urology, Faculty of Medicine, Cipto Mangunkusumo Hospital, University of Indonesia, Jalan Diponegoro No. 71, Jakarta, 10430, Indonesia, 62 21 150 0135, anggariskyraharja@gmail.com %K chordee %K superficial chordee %K COL1A1 %K COL3A1 %K dartos tissue %K dartos fascia %K ELN %K elastin %K histopathological %D 2025 %7 18.2.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: The exact cause of penile curvature in hypospadias remains unknown. Resection of the dartos fascia has been observed to straighten the penis, indicating the involvement of the dartos fascia in the superficial chordee. However, the characteristics of dartos tissue in the distal territory of the ventral penile shaft may differ from those in the proximal aspect of the penile shaft. Objective: This study aims to investigate the distinct histopathological profiles and expression of COL1A1 (collagen type 1), COL3A1 (collagen type 3), and ELN (elastin) in proximal and distal ventral dartos of patients with hypospadias compared to those without hypospadias. Methods: This prospective case-control study compares the ventral dartos tissue of patients with hypospadias at different locations with that of patients without hypospadias. Dartos samples will be taken during surgery, with age matching. Histopathology examination uses hematoxylin and eosin and Masson’s trichrome stain. The mRNA expression of COL1A1, COL3A1, and ELN will be quantified using a 2-step reverse transcription–polymerase chain reaction analysis. Results: Previous studies have documented different characteristics of dartos tissue between patients with hypospadias and those without hypospadias. Some studies even suggest resection of the dartos tissue during hypospadias repair. However, this is the first study to compare the characteristics of ventral dartos tissue in patients with hypospadias based on its location along the penile shaft, suggesting potential differences between the distal and proximal locations. We have obtained ethical approval to conduct a prospective case-control study aimed at elucidating these differences in dartos tissue characteristics. The findings of the study are anticipated to be available by 2025. Conclusions: Differences in the characteristics of dartos fascia based on its location may require tailored surgical strategies. If the properties of distal dartos tissue closely mirror those of typical dartos tissue, the possibility of avoiding its excision during hypospadias surgery could be considered. International Registered Report Identifier (IRRID): DERR1-10.2196/70075 %M 39964742 %R 10.2196/70075 %U https://www.researchprotocols.org/2025/1/e70075 %U https://doi.org/10.2196/70075 %U http://www.ncbi.nlm.nih.gov/pubmed/39964742 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e48775 %T Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study %A Bhavnani,Suresh K %A Zhang,Weibin %A Bao,Daniel %A Raji,Mukaila %A Ajewole,Veronica %A Hunter,Rodney %A Kuo,Yong-Fang %A Schmidt,Susanne %A Pappadis,Monique R %A Smith,Elise %A Bokov,Alex %A Reistetter,Timothy %A Visweswaran,Shyam %A Downer,Brian %+ School of Public and Population Health, Department of Biostatistics & Data Science, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, United States, 1 (734) 772 1929, subhavna@utmb.edu %K social determinants of health %K All of Us %K bipartite networks %K financial resources %K health care %K health outcomes %K precision medicine %K decision support %K health industry %K clinical implications %K machine learning methods %D 2025 %7 11.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Social determinants of health (SDoH), such as financial resources and housing stability, account for between 30% and 55% of people’s health outcomes. While many studies have identified strong associations between specific SDoH and health outcomes, little is known about how SDoH co-occur to form subtypes critical for designing targeted interventions. Such analysis has only now become possible through the All of Us program. Objective: This study aims to analyze the All of Us dataset for addressing two research questions: (1) What are the range of and responses to survey questions related to SDoH? and (2) How do SDoH co-occur to form subtypes, and what are their risks for adverse health outcomes? Methods: For question 1, an expert panel analyzed the range of and responses to SDoH questions across 6 surveys in the full All of Us dataset (N=372,397; version 6). For question 2, due to systematic missingness and uneven granularity of questions across the surveys, we selected all participants with valid and complete SDoH data and used inverse probability weighting to adjust their imbalance in demographics. Next, an expert panel grouped the SDoH questions into SDoH factors to enable more consistent granularity. To identify the subtypes, we used bipartite modularity maximization for identifying SDoH biclusters and measured their significance and replicability. Next, we measured their association with 3 outcomes (depression, delayed medical care, and emergency room visits in the last year). Finally, the expert panel inferred the subtype labels, potential mechanisms, and targeted interventions. Results: The question 1 analysis identified 110 SDoH questions across 4 surveys covering all 5 domains in Healthy People 2030. As the SDoH questions varied in granularity, they were categorized by an expert panel into 18 SDoH factors. The question 2 analysis (n=12,913; d=18) identified 4 biclusters with significant biclusteredness (Q=0.13; random-Q=0.11; z=7.5; P<.001) and significant replication (real Rand index=0.88; random Rand index=0.62; P<.001). Each subtype had significant associations with specific outcomes and had meaningful interpretations and potential targeted interventions. For example, the Socioeconomic barriers subtype included 6 SDoH factors (eg, not employed and food insecurity) and had a significantly higher odds ratio (4.2, 95% CI 3.5-5.1; P<.001) for depression when compared to other subtypes. The expert panel inferred implications of the results for designing interventions and health care policies based on SDoH subtypes. Conclusions: This study identified SDoH subtypes that had statistically significant biclusteredness and replicability, each of which had significant associations with specific adverse health outcomes and with translational implications for targeted SDoH interventions and health care policies. However, the high degree of systematic missingness requires repeating the analysis as the data become more complete by using our generalizable and scalable machine learning code available on the All of Us workbench. %M 39932771 %R 10.2196/48775 %U https://www.jmir.org/2025/1/e48775 %U https://doi.org/10.2196/48775 %U http://www.ncbi.nlm.nih.gov/pubmed/39932771 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59841 %T Trends and Gaps in Digital Precision Hypertension Management: Scoping Review %A Clifford,Namuun %A Tunis,Rachel %A Ariyo,Adetimilehin %A Yu,Haoxiang %A Rhee,Hyekyun %A Radhakrishnan,Kavita %+ , School of Nursing, The University of Texas at Austin, 1710 Red River St, Austin, TX, 78701, United States, 1 (512) 471 7913, namuun.clifford@utexas.edu %K precision health %K hypertension %K digital health %K prediction models %K personalization %K phenotyping %K machine learning %K algorithms %K mobile apps %K mobile health %D 2025 %7 10.2.2025 %9 Review %J J Med Internet Res %G English %X Background: Hypertension (HTN) is the leading cause of cardiovascular disease morbidity and mortality worldwide. Despite effective treatments, most people with HTN do not have their blood pressure under control. Precision health strategies emphasizing predictive, preventive, and personalized care through digital tools offer notable opportunities to optimize the management of HTN. Objective: This scoping review aimed to fill a research gap in understanding the current state of precision health research using digital tools for the management of HTN in adults. Methods: This study used a scoping review framework to systematically search for articles in 5 databases published between 2013 and 2023. The included articles were thematically analyzed based on their precision health focus: personalized interventions, prediction models, and phenotyping. Data were extracted and summarized for study and sample characteristics, precision health focus, digital health technology, disciplines involved, and characteristics of personalized interventions. Results: After screening 883 articles, 46 were included; most studies had a precision health focus on personalized digital interventions (34/46, 74%), followed by prediction models (8/46, 17%) and phenotyping (4/46, 9%). Most studies (38/46, 82%) were conducted in or used data from North America or Europe, and 63% (29/46) of the studies came exclusively from the medical and health sciences, with 33% (15/46) of studies involving 2 or more disciplines. The most commonly used digital technologies were mobile phones (33/46, 72%), blood pressure monitors (18/46, 39%), and machine learning algorithms (11/46, 24%). In total, 45% (21/46) of the studies either did not report race or ethnicity data (14/46, 30%) or partially reported this information (7/46, 15%). For personalized intervention studies, nearly half (14/30, 47%) used 2 or less types of data for personalization, with only 7% (2/30) of the studies using social determinants of health data and no studies using physical environment or digital literacy data. Personalization characteristics of studies varied, with 43% (13/30) of studies using fully automated personalization approaches, 33% (10/30) using human-driven personalization, and 23% (7/30) using a hybrid approach. Conclusions: This scoping review provides a comprehensive mapping of the literature on the current trends and gaps in digital precision health research for the management of HTN in adults. Personalized digital interventions were the primary focus of most studies; however, the review highlighted the need for more precise definitions of personalization and the integration of more diverse data sources to improve the tailoring of interventions and promotion of health equity. In addition, there were significant gaps in the reporting of race and ethnicity data of participants, underuse of wearable devices for passive data collection, and the need for greater interdisciplinary collaboration to advance precision health research in digital HTN management. Trial Registration: OSF Registries osf.io/yuzf8; https://osf.io/yuzf8 %M 39928934 %R 10.2196/59841 %U https://www.jmir.org/2025/1/e59841 %U https://doi.org/10.2196/59841 %U http://www.ncbi.nlm.nih.gov/pubmed/39928934 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e68371 %T Identifying High-Priority Ethical Challenges for Precision Emergency Medicine: Nominal Group Study %A Rose,Christian %A Shearer,Emily %A Woller,Isabela %A Foster,Ashley %A Ashenburg,Nicholas %A Kim,Ireh %A Newberry,Jennifer %K precision medicine %K emergency medicine %K ethical considerations %K nominal group study %K consensus framework %D 2025 %7 6.2.2025 %9 %J JMIR Form Res %G English %X Background: Precision medicine promises to revolutionize health care by providing the right care to the right patient at the right time. However, the emergency department’s unique mandate to treat “anyone, anywhere, anytime” creates critical tensions with precision medicine’s requirements for comprehensive patient data and computational analysis. As emergency departments serve as health care’s safety net and provide a growing proportion of acute care in America, identifying and addressing the ethical challenges of implementing precision medicine in this setting is crucial to prevent exacerbation of existing health care disparities. The rapid advancement of precision medicine technologies makes it imperative to understand these challenges before widespread implementation in emergency care settings. Objective: This study aimed to identify high priority ethical concerns facing the implementation of precision medicine in the emergency department. Methods: We conducted a qualitative study using a modified nominal group technique (NGT) with emergency physicians who had previous knowledge of precision medicine concepts. The NGT process consisted of four phases: (1) silent generation of ideas, (2) round-robin sharing of ideas, (3) structured discussion and clarification, and (4) thematic grouping of priorities. Participants represented diverse practice settings (county hospital, community hospital, academic center, and integrated managed care consortium) and subspecialties (education, ethics, pediatrics, diversity, equity, inclusion, and informatics) across various career stages from residents to late-career physicians. Results: A total of 12 emergency physicians identified 82 initial challenges during individual ideation, which were consolidated to 48 unique challenges after removing duplicates and combining related items. The average participant contributed 6.8 (SD 2.9) challenges. These challenges were organized into a framework with 3 themes: values, privacy, and justice. The framework identified the need to address these themes across 3 time points of the precision medicine process: acquisition of data, actualization in the care setting, and the after effects of its use. This systematic organization revealed interrelated concerns spanning from data collection and bias to implementation challenges and long-term consequences for health care equity. Conclusions: Our study developed a novel framework that maps critical ethical challenges across 3 domains (values, privacy, and justice) and 3 temporal stages of precision medicine implementation. This framework identifies high-priority areas for future research and policy development, particularly around data representation, privacy protection, and equitable access. Successfully addressing these challenges is essential to realize precision medicine’s potential while preserving emergency medicine’s core mission as health care’s safety net. %R 10.2196/68371 %U https://formative.jmir.org/2025/1/e68371 %U https://doi.org/10.2196/68371 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e67346 %T Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study %A Kou,Yanqi %A Ye,Shicai %A Tian,Yuan %A Yang,Ke %A Qin,Ling %A Huang,Zhe %A Luo,Botao %A Ha,Yanping %A Zhan,Liping %A Ye,Ruyin %A Huang,Yujie %A Zhang,Qing %A He,Kun %A Liang,Mouji %A Zheng,Jieming %A Huang,Haoyuan %A Wu,Chunyi %A Ge,Lei %A Yang,Yuping %+ Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, No. 2 Wenming East Road, Xiashan, Zhanjiang, Zhanjiang, 524000, China, 1 13106629993, yangyupingchn@163.com %K acute myocardial infarction %K gastrointestinal bleeding %K machine learning %K in-hospital %K prediction model %D 2025 %7 30.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making. Objective: This study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. Methods: A multicenter retrospective cohort study was conducted, including 1910 patients with AMI from the Affiliated Hospital of Guangdong Medical University (2005-2024). Patients were divided into training (n=1575) and testing (n=335) cohorts based on admission dates. For external validation, 1746 patients with AMI were included in the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. The models were evaluated for the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, and decision curve analysis. Shapley additive explanations analysis ranked variable importance. Kaplan-Meier survival analysis evaluated the impact of GIB on short-term survival. Multivariate logistic regression assessed the relationship between coronary heart disease (CHD) and in-hospital GIB after adjusting for clinical variables. Results: The RF model outperformed other ML models, achieving an area under the receiver operating characteristic curve of 0.77 in the training cohort, 0.77 in the testing cohort, and 0.75 in the validation cohort. Key predictors included red blood cell count, hemoglobin, maximal myoglobin, hematocrit, CHD, and other variables, all of which were strongly associated with GIB risk. Decision curve analysis demonstrated the clinical use of the RF model for early risk stratification. Kaplan-Meier survival analysis showed no significant differences in 7- and 15-day survival rates between patients with AMI with and without GIB (P=.83 for 7-day survival and P=.87 for 15-day survival). Multivariate logistic regression showed that CHD was an independent risk factor for in-hospital GIB (odds ratio 2.79, 95% CI 2.09-3.74). Stratified analyses by sex, age, occupation, marital status, and other subgroups consistently showed that the association between CHD and GIB remained robust across all subgroups. Conclusions: The ML-based RF model provides a robust and clinically applicable tool for predicting in-hospital GIB in patients with AMI. By leveraging routinely available clinical and laboratory data, the model supports early risk stratification and personalized preventive strategies. %M 39883922 %R 10.2196/67346 %U https://www.jmir.org/2025/1/e67346 %U https://doi.org/10.2196/67346 %U http://www.ncbi.nlm.nih.gov/pubmed/39883922 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65974 %T Impact of Online Interactive Decision Tools on Women’s Decision-Making Regarding Breast Cancer Screening: Systematic Review and Meta-Analysis %A Villain,Patricia %A Downham,Laura %A Le Bonniec,Alice %A Bauquier,Charlotte %A Mandrik,Olena %A Nadarzynski,Tom %A Donelle,Lorie %A Murillo,Raúl %A Tolma,Eleni L %A Johnson,Sonali %A Soler-Michel,Patricia %A Smith,Robert %+ International Agency for Research on Cancer, World Health Organization, 25 Avenue Tony Garnier CS 90627, Lyon, 69366, France, 33 4 72 73 84 40, patriciavillain1@gmail.com %K breast cancer screening %K decision-making %K online interactive %K decision aid %K average risk %K shared decision-making %K screening participation %K cognitive determinants %K women %D 2025 %7 29.1.2025 %9 Review %J J Med Internet Res %G English %X Background: The online nature of decision aids (DAs) and related e-tools supporting women’s decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs. Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy. Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023. We included studies reporting on populations at average risk of breast cancer, which utilized DAs or related e-tools, and assessed women’s participation in BCS by mammography or other key cognitive determinants of decision-making as primary or secondary outcomes. We conducted meta-analyses on the identified randomized controlled trials, which were assessed using the revised Cochrane Risk of Bias 2 (RoB 2) tool. We further explored intermediate and high heterogeneity between studies to enhance the validity of our results. Results: In total, 22 different e-tools were identified across 31 papers. The degree of tailoring in the e-tools, specifically whether the tool was fully tailored or featured with tailoring, was the most influential factor in women’s decision-making regarding BCS. Compared with control groups, tailored e-tools significantly increased women’s long-term participation in BCS (risk ratio 1.14, 95% CI 1.07-1.23, P<.001, I2=0%). Tailored-to-breast-cancer-risk e-tools increased women’s level of worry (mean difference 0.31, 95% CI 0.13-0.48, P<.001, I2=0%). E-tools also improved women’s adequate knowledge of BCS, with features-with-tailoring e-tools designed and tested with the general population being more effective than tailored e-tools designed for or tested with non-BCS participants (χ21=5.1, P=.02). Features-with-tailoring e-tools increased both the rate of women who intended not to undergo BCS (risk ratio 1.88, 95% CI 1.43-2.48, P<.001, I2=0%) and the rate of women who had made an informed choice regarding their intention to undergo BCS (risk ratio 1.60, 95% CI 1.09-2.33, P=.02, I2=91%). Additionally, these tools decreased the proportion of women with decision conflict (risk ratio 0.77, 95% CI 0.65-0.91, P=.002, I2=0%). Shared decision-making was not formally evaluated. This review is limited by small sample sizes, including only a few studies in the meta-analysis, some with a high risk of bias, and high heterogeneity between the studies and e-tools. Conclusions: Features-with-tailoring e-tools could potentially negatively impact BCS programs by fostering negative intentions and attitudes toward BCS participation. Conversely, tailored e-tools may increase women’s participation in BCS but, when tailored to risk, they may elevate their levels of worry. To maximize the effectiveness of e-tools while minimizing potential negative impacts, we advocate for an “on-demand” layered approach to their design. %M 39879616 %R 10.2196/65974 %U https://www.jmir.org/2025/1/e65974 %U https://doi.org/10.2196/65974 %U http://www.ncbi.nlm.nih.gov/pubmed/39879616 %0 Journal Article %@ 2563-6316 %I JMIR Publications %V 6 %N %P e50712 %T Identifying Safeguards Disabled by Epstein-Barr Virus Infections in Genomes From Patients With Breast Cancer: Chromosomal Bioinformatics Analysis %A Friedenson,Bernard %K breast cancer %K cancer %K oncology %K ovarian %K virus %K viral %K Epstein-Barr %K herpes %K bioinformatics %K chromosome %K gene %K genetic %K chromosomal %K DNA %K genomic %K BRCA %K metastasis %K biology %D 2025 %7 29.1.2025 %9 %J JMIRx Med %G English %X Background: The causes of breast cancer are poorly understood. A potential risk factor is Epstein-Barr virus (EBV), a lifelong infection nearly everyone acquires. EBV-transformed human mammary cells accelerate breast cancer when transplanted into immunosuppressed mice, but the virus can disappear as malignant cells reproduce. If this model applies to human breast cancers, then they should have genome damage characteristic of EBV infection. Objective: This study tests the hypothesis that EBV infection predisposes one to breast cancer by causing permanent genome damage that compromises cancer safeguards. Methods: Publicly available genome data from approximately 2100 breast cancers and 25 ovarian cancers were compared to cancers with proven associations to EBV, including 70 nasopharyngeal cancers, 90 Burkitt lymphomas, 88 diffuse large B-cell lymphomas, and 34 gastric cancers. Calculation algorithms to make these comparisons were developed. Results: Chromosome breakpoints in breast and ovarian cancer clustered around breakpoints in EBV-associated cancers. Breakpoint distributions in breast and EBV-associated cancers on some chromosomes were not confidently distinguished (P>.05), but differed from controls unrelated to EBV infection. Viral breakpoint clusters occurred in high-risk, sporadic, and other breast cancer subgroups. Breakpoint clusters disrupted gene functions essential for cancer protection, which remain compromised even if EBV infection disappears. As CRISPR (clustered regularly interspaced short palindromic repeats)–like reminders of past infection during evolution, EBV genome fragments were found regularly interspaced between Piwi-interacting RNA (piRNA) genes on chromosome 6. Both breast and EBV-associated cancers had inactivated genes that guard piRNA defenses and the major histocompatibility complex (MHC) locus. Breast and EBV-associated cancer breakpoints and other variations converged around the highly polymorphic MHC. Not everyone develops cancer because MHC differences produce differing responses to EBV infection. Chromosome shattering and mutation hot spots in breast cancers preferentially occurred at incorporated viral sequences. On chromosome 17, breast cancer breakpoints that clustered around those in EBV-mediated cancers were linked to estrogen effects. Other breast cancer breaks affected sites where EBV inhibits JAK-STAT and SWI-SNF signaling pathways. A characteristic EBV-cancer gene deletion that shifts metabolism to favor tumors was also found in breast cancers. These changes push breast cancer into metastasis and then favor survival of metastatic cells. Conclusions: EBV infection predisposes one to breast cancer and metastasis, even if the virus disappears. Identifying this pathogenic viral damage may improve screening, treatment, and prevention. Immunizing children against EBV may protect against breast, ovarian, other cancers, and potentially even chronic unexplained diseases. %R 10.2196/50712 %U https://xmed.jmir.org/2025/1/e50712 %U https://doi.org/10.2196/50712 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e54609 %T Evaluation of the Feasibility of Transfusing Leukocyte Depletion Filter–Processed Intraoperative Cell Salvage Blood in Metastatic Spine Tumor Surgery: Protocol for a Non–Randomized Study %A Kumar,Naresh %A Hui,Si Jian %A Lee,Renick %A Athia,Sahil %A Tan,Joel Yong Hao %A Tan,Jonathan Jiong Hao %+ Department of Orthopaedic Surgery, National University Hospital, National University Health System, 1E Kent Ridge Rd, Singapore, 119228, Singapore, 65 67725611, dosksn@nus.edu.sg %K blood transfusion %K autologous blood transfusion %K operative blood salvage %K leukocyte reduction filtration %K intraoperative blood cell salvage %K extramedullary spinal cord compression %K metastases %K tumors %K leukocytes %D 2025 %7 17.1.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Metastatic spine tumor surgery (MSTS) is often complex and extensive leading to significant blood loss. Allogeneic blood transfusion (ABT) is the mainstay of blood replenishment but with immune-mediated postoperative complications. Alternative blood management techniques (salvaged blood transfusion [SBT]) allow us to overcome such complications. Despite widespread use of intraoperative cell salvage (IOCS) in oncological and nononcological surgical procedures, surgeons remain reluctant to use IOCS in MSTS. Objective: This study aims to analyze safety of IOCS-leukocyte depletion filter (LDF)–processed blood transfusion for patients undergoing MSTS by assessing clinical outcomes—disease progression: tumor progression and overall survival. This study evaluates whether reinfusion of IOCS-LDF–processed blood reduces ABT rates in patients undergoing MSTS by sorting patients undergoing MSTS who require ABT into patients who consent to receive or not receive SBT. Methods: We aim to recruit a minimum of 90 patients—30 patients for SBT, 30 patients for ABT, and 30 patients with no blood transfusion. SBT and ABT form the 2 experimental arms, whereas no blood transfusion forms the control cohort. Available patient data will be reviewed to determine tumor burden secondary to metastasis and postoperative survival and disease progression, improvement in pain, and neurological and ambulatory status. Data collected will be studied postoperatively at 3, 6, 12, 24, 36, and 48 months or until demise, whichever occurs first. Outcomes of the experimental groups will be compared with those of the control group. Outcomes will be analyzed using 1-way ANOVA and Fisher exact test. The Kaplan-Meier curve and a log-rank test will be used to study overall survival. A multivariate and competing risk analysis will be used to study the association between blood transfusion type and tumor progression. All statistical analyses will be done using Stata Special Edition 14.0 (StataCorp LP). Results: This is the largest clinical study on use of IOCS in MSTS from various primary malignancies to date. It will provide significant clinical evidence regarding the safety and applicability of IOCS in MSTS. It will help reduce use of ABT, improving overall blood management of patients undergoing MSTS. A limitation of this study is that not all patients undergoing MSTS will survive for the follow-up period (4 years), theoretically leading to underreporting of disease progression. Study commenced in 2016 and patient recruitment continued till 2019. As of September 2019, we have collected operative data on 140 patients. However, the 2-year outcomes of about 40.0% (56/140) of patients are in the process of collection. The study is aimed to be published in the years 2023-2024. Conclusions: Results will be disseminated via peer-reviewed publications, paving the way for future studies. International Registered Report Identifier (IRRID): DERR1-10.2196/54609 %M 39823595 %R 10.2196/54609 %U https://www.researchprotocols.org/2025/1/e54609 %U https://doi.org/10.2196/54609 %U http://www.ncbi.nlm.nih.gov/pubmed/39823595 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e60189 %T Building a Digital Health Research Platform to Enable Recruitment, Enrollment, Data Collection, and Follow-Up for a Highly Diverse Longitudinal US Cohort of 1 Million People in the All of Us Research Program: Design and Implementation Study %A Klein,Dave %A Montgomery,Aisha %A Begale,Mark %A Sutherland,Scott %A Sawyer,Sherilyn %A McCauley,Jacob L %A Husbands,Letheshia %A Joshi,Deepti %A Ashbeck,Alan %A Palmer,Marcy %A Jain,Praduman %+ Vibrent Health, Inc, 4114 Legato Rd #900, Fairfax, VA, 22033, United States, 1 6784686545, aisha.montgomery@gmail.com %K longitudinal studies %K cohort studies %K health disparities %K minority populations %K vulnerable populations %K precision medicine %K biomedical research %K decentralization %K digital health technology %K database management system %D 2025 %7 15.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Longitudinal cohort studies have traditionally relied on clinic-based recruitment models, which limit cohort diversity and the generalizability of research outcomes. Digital research platforms can be used to increase participant access, improve study engagement, streamline data collection, and increase data quality; however, the efficacy and sustainability of digitally enabled studies rely heavily on the design, implementation, and management of the digital platform being used. Objective: We sought to design and build a secure, privacy-preserving, validated, participant-centric digital health research platform (DHRP) to recruit and enroll participants, collect multimodal data, and engage participants from diverse backgrounds in the National Institutes of Health’s (NIH) All of Us Research Program (AOU). AOU is an ongoing national, multiyear study aimed to build a research cohort of 1 million participants that reflects the diversity of the United States, including minority, health-disparate, and other populations underrepresented in biomedical research (UBR). Methods: We collaborated with community members, health care provider organizations (HPOs), and NIH leadership to design, build, and validate a secure, feature-rich digital platform to facilitate multisite, hybrid, and remote study participation and multimodal data collection in AOU. Participants were recruited by in-person, print, and online digital campaigns. Participants securely accessed the DHRP via web and mobile apps, either independently or with research staff support. The participant-facing tool facilitated electronic informed consent (eConsent), multisource data collection (eg, surveys, genomic results, wearables, and electronic health records [EHRs]), and ongoing participant engagement. We also built tools for research staff to conduct remote participant support, study workflow management, participant tracking, data analytics, data harmonization, and data management. Results: We built a secure, participant-centric DHRP with engaging functionality used to recruit, engage, and collect data from 705,719 diverse participants throughout the United States. As of April 2024, 87% (n=613,976) of the participants enrolled via the platform were from UBR groups, including racial and ethnic minorities (n=282,429, 46%), rural dwelling individuals (n=49,118, 8%), those over the age of 65 years (n=190,333, 31%), and individuals with low socioeconomic status (n=122,795, 20%). Conclusions: We built a participant-centric digital platform with tools to enable engagement with individuals from different racial, ethnic, and socioeconomic backgrounds and other UBR groups. This DHRP demonstrated successful use among diverse participants. These findings could be used as best practices for the effective use of digital platforms to build and sustain cohorts of various study designs and increase engagement with diverse populations in health research. %M 39813673 %R 10.2196/60189 %U https://www.jmir.org/2025/1/e60189 %U https://doi.org/10.2196/60189 %U http://www.ncbi.nlm.nih.gov/pubmed/39813673 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60535 %T Effect of a Feedback Visit and a Clinical Decision Support System Based on Antibiotic Prescription Audit in Primary Care: Multiarm Cluster-Randomized Controlled Trial %A Jeanmougin,Pauline %A Larramendy,Stéphanie %A Fournier,Jean-Pascal %A Gaultier,Aurélie %A Rat,Cédric %+ Department of General Practice, Faculty of Medicine, Nantes University, 1, rue Gaston-Veil, Nantes, 44000, France, 33 02 40 41 11 29, pauline.jeanmougin@univ-nantes.fr %K antibacterial agents %K feedback %K clinical decision support system %K prescriptions %K primary health care %K clinical decision %K antibiotic prescription %K antimicrobial %K antibiotic stewardship %K interventions %K health insurance %K systematic antibiotic prescriptions %D 2024 %7 18.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is needed to optimize future interventions. Objective: This study aimed to evaluate 2 multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care. Methods: An open-label, cluster-randomized controlled trial of 2501 general practitioners (GPs) working in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention, consisting of a visit by a health insurance representative who gave prescription feedback and provided a leaflet for treating cystitis and tonsillitis; and a clinical decision support system (CDSS)–based intervention, consisting of a visit with prescription feedback and a CDSS demonstration on antibiotic prescribing. The control group received no intervention. Data on systemic antibiotic dispensing was obtained from the National Health Insurance System (Système National d’Information Inter-Régimes de l’Assurance Maladie) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using a 2-level hierarchical analysis of covariance adjusted for annual antibiotic prescription volume at baseline. Results: Overall, 2501 GPs were randomized (n=1099, 43.9% women). At 12 months, the mean volume of systemic antibiotics per GP decreased by 219.2 (SD 61.4; 95% CI −339.5 to −98.8; P<.001) defined daily doses in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systemic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (−109.7, SD 62.4; 95% CI −232.0 to 12.5 defined daily doses; P=.08). Conclusions: A visit by a health insurance representative combining feedback and a CDSS demonstration resulted in a 4.4% (-219.2/4930) reduction in the total volume of systemic antibiotic prescriptions in 12 months. Trial Registration: ClinicalTrials.gov NCT04028830; https://clinicaltrials.gov/study/NCT04028830 %M 39693139 %R 10.2196/60535 %U https://www.jmir.org/2024/1/e60535 %U https://doi.org/10.2196/60535 %U http://www.ncbi.nlm.nih.gov/pubmed/39693139 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e52107 %T Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach %A Thanh Phuc,Phan %A Nguyen,Phung-Anh %A Nguyen,Nam Nhat %A Hsu,Min-Huei %A Le,Nguyen Quoc Khanh %A Tran,Quoc-Viet %A Huang,Chih-Wei %A Yang,Hsuan-Chia %A Chen,Cheng-Yu %A Le,Thi Anh Hoa %A Le,Minh Khoi %A Nguyen,Hoang Bac %A Lu,Christine Y %A Hsu,Jason C %+ College of Management, Taipei Medical University, 11F. Biomedical Technology Building, No. 301, Yuantong Rd., Zhonghe Dist, New Taipei, 235, Taiwan, 886 2 66202589 ext 16119, jasonhsu@tmu.edu.tw %K diabetes %K dementia %K machine learning %K prediction model %K TMUCRD %K Taipei Medical University Clinical Research Database %D 2024 %7 11.12.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences. Objective: This study aimed to develop a personalized predictive model, using artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with type 2 diabetes mellitus (T2DM) who are prescribed antidiabetic medications. Methods: This retrospective multicenter study used data from the Taipei Medical University Clinical Research Database, which comprises electronic medical records from 3 hospitals in Taiwan. This study applied 8 machine learning algorithms to develop prediction models, including logistic regression, linear discriminant analysis, gradient boosting machine, light gradient boosting machine, AdaBoost, random forest, extreme gradient boosting, and artificial neural network (ANN). These models incorporated a range of variables, encompassing patient characteristics, comorbidities, medication usage, laboratory results, and examination data. Results: This study involved a cohort of 43,068 patients diagnosed with type 2 diabetes mellitus, which accounted for a total of 1,937,692 visits. For model development and validation, 1,300,829 visits were used, while an additional 636,863 visits were reserved for external testing. The area under the curve of the prediction models range from 0.67 for the logistic regression to 0.98 for the ANNs. Based on the external test results, the model built using the ANN algorithm had the best area under the curve (0.97 for 5-year follow-up period and 0.98 for 10-year follow-up period). Based on the best model (ANN), age, gender, triglyceride, hemoglobin A1c, antidiabetic agents, stroke history, and other long-term medications were the most important predictors. Conclusions: We have successfully developed a novel, computer-aided, dementia risk prediction model that can facilitate the clinical diagnosis and management of patients prescribed with antidiabetic medications. However, further investigation is required to assess the model’s feasibility and external validity. %M 39434474 %R 10.2196/52107 %U https://www.jmir.org/2024/1/e52107 %U https://doi.org/10.2196/52107 %U http://www.ncbi.nlm.nih.gov/pubmed/39434474 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e57312 %T A 360° Approach to Personalize Lifestyle Treatment in Primary Care for People With Type 2 Diabetes: Feasibility Study %A Harakeh,Zeena %A de Hoogh,Iris %A Krijger-Dijkema,Anne-Margreeth %A Berbée,Susanne %A Kalkman,Gino %A van Empelen,Pepijn %A Otten,Wilma %+ Department of Child Health, TNO, Netherlands Organization for Applied Scientific Research, Sylviusweg 71, Leiden, 2333 BE, Netherlands, 31 611615907, zeena.harakeh@tno.nl %K type 2 diabetes %K diagnostic tool %K holistic approach %K personalized treatment %K shared decision-making %K health professionals %K intervention %K feasibility study %K primary care %D 2024 %7 4.12.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Given the multifactorial nature of type 2 diabetes (T2D), health care for this condition would benefit from a holistic approach and multidisciplinary consultation. To address this, we developed the web-based 360-degree (360°) diagnostic tool, which assesses 4 key domains: “body” (physical health parameters), “thinking and feeling” (eg, mental health and stress), “behavior” (lifestyle factors), and “environment” (eg, work and housing conditions). Objective: This work examines the acceptability, implementation, and potential effects of the 360° diagnostic tool and subsequent tailored treatment (360° approach) in a 6-month intervention and feasibility study conducted in standard primary health care settings in the Netherlands. Methods: A single-group design with baseline, 3-month, and 6-month follow-ups was used. A total of 15 people with T2D and their health care providers from 2 practices participated in a 6-month intervention, which included the 360° diagnosis, tailored treatment, and both individual and group consultations. The 360° diagnosis involved clinical measurements for the “body” domain and self-reports for the “thinking and feeling,” “behavior,” and “environment” domains. After multidisciplinary consultations involving the general practitioner, pharmacist, nurse practitioner (NP), and dietitian, the NP and dietitian provided tailored advice, lifestyle treatment, and ongoing support. At the end of the intervention, face-to-face semistructured interviews were conducted with health care professionals (n=6) and participants (n=13) to assess the acceptability and implementation of the 360° approach in primary health care. Additionally, data from 14 participants on the “thinking and feeling” and “behavior” domains at baseline, 3 months, and 6 months were analyzed to assess changes over time. Results: The semistructured interviews revealed that both participants with T2D and health care professionals were generally positive about various aspects of the 360° approach, including onboarding, data collection with the 360° diagnosis, consultations and advice from the NP and dietitian, the visual representation of parameters in the profile wheel, counseling during the intervention (including professional collaboration), and the group meetings. The interviews also identified factors that promoted or hindered the implementation of the 360° approach. Promoting factors included (1) the care, attention, support, and experience of professionals; (2) the multidisciplinary team; (3) social support; and (4) the experience of positive health effects. Hindering factors included (1) too much information, (2) survey-related issues, and (3) time-consuming counseling. In terms of effects over time, improvements were observed at 3 months in mental health, diabetes-related problems, and fast-food consumption. At 6 months, there was a reduction in perceived stress and fast-food consumption. Additionally, fruit intake decreased at both 3 and 6 months. Conclusions: Our findings suggest that the 360° approach is acceptable to both people with T2D and health care professionals, implementable, and potentially effective in fostering positive health changes. Overall, it appears feasible to implement the 360° approach in standard primary health care. Trial Registration: Netherlands Trial Register NL-7509/NL-OMON45788; https://onderzoekmetmensen.nl/nl/trial/45788 %M 39631068 %R 10.2196/57312 %U https://formative.jmir.org/2024/1/e57312 %U https://doi.org/10.2196/57312 %U http://www.ncbi.nlm.nih.gov/pubmed/39631068 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e56553 %T An Educational Digital Tool to Improve the Implementation of Switching to a Biosimilar (Rapid Switch Trainer): Tool Development and Validation Study %A Marras,Carlos %A Labarga,María %A Ginard,Daniel %A Carrascosa,Jose Manuel %A Escudero-Contreras,Alejandro %A Collantes-Estevez,Eduardo %A de Mora,Fernando %A Robles,Tamara %A Romero,Elisa %A Martínez,Rafael %K consumer health information %K treatment switching %K biosimilar pharmaceuticals %K immune-mediated diseases %K education %K qualitative research %K training %K nocebo %K digital tool %K implementation %D 2024 %7 21.11.2024 %9 %J JMIR Form Res %G English %X Background: Switching to biosimilars is an effective and safe practice in treating inflammatory diseases; however, a nocebo effect may arise as a result of the way in which the switch is communicated to a given patient. Objective: We aimed to design a gaming-based digital educational tool (including a discussion algorithm) to support the training of health care professionals in efficiently communicating the switch to biosimilars, minimizing the generation of a nocebo effect and thus serving as an implementation strategy for the recommended switch. Methods: The tool was developed based on interviews and focus group discussions with key stakeholders, both patients and health care professionals. Messages likely to either generate trust or to trigger a nocebo effect were generated on the basis of the interviews and focus group discussions. Results: A total 7 clinicians and 4 nurses specializing in rheumatology, gastroenterology, and dermatology, with balanced levels of responsibility and experience, as well as balance between geographic regions, participated in the structured direct interviews and provided a list of arguments they commonly used, or saw used, to justify the switching, and objections given by the patients they attended. Patients with immune-mediated inflammatory diseases who were taking biologic drugs with (n=4) and without (n=5) experience in switching attended the focus groups and interviews. Major topics of discussion were the reason for the change, the nature of biosimilars, and their quality, safety, efficacy, and cost. Based on these discussions, a list of objections and of potential arguments was produced. Patients and health care professionals rated the arguments for their potential to evoke trust or a nocebo effect. Two sets of arguments, related to savings and sustainability, showed discrepant ratings between patients and health care professionals. Objections and arguments were organized by categories and incorporated into the tool as algorithms. The educators then developed additional arguments (with inadequate answers) to complement the valid ones worked on in the focus groups. The tool was then developed as a collection of clinical situations or vignettes that appear randomly to the user, who then has to choose an argument to counteract the given objections. After each interaction, the tool provides feedback. The tool was further supported by accredited medical training on biosimilars and switching. Conclusions: We have developed a digital training tool to improve communication on switching to biosimilars in the clinic and prevent a nocebo effect based on broad and in-depth experiences of patients and health care professionals. The validation of this implementation strategy is ongoing. %R 10.2196/56553 %U https://formative.jmir.org/2024/1/e56553 %U https://doi.org/10.2196/56553 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58504 %T Definitions and Characteristics of Patient Digital Twins Being Developed for Clinical Use: Scoping Review %A Drummond,David %A Gonsard,Apolline %+ Health Data- and Model-Driven Knowledge Acquisition Team, National Institute for Research in Digital Science and Technology, 2 Rue Simone IFF, Paris, 75012, France, 33 144494848, david.drummond@aphp.fr %K patient simulation %K cyber-physical systems %K telemonitoring %K personalized medicine %K precision medicine %K digital twin %D 2024 %7 13.11.2024 %9 Review %J J Med Internet Res %G English %X Background: The concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient. Objective: The objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature. Methods: We searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed. Results: We identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining “digital twin,” 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization. Conclusions: We propose defining a patient digital twin as “a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions” and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing the potential of these personalized, integrative technologies to advance clinical care. %M 39536311 %R 10.2196/58504 %U https://www.jmir.org/2024/1/e58504 %U https://doi.org/10.2196/58504 %U http://www.ncbi.nlm.nih.gov/pubmed/39536311 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e60787 %T Exploration of Features of Mobile Applications for Medication Adherence in Asia: Narrative Review %A Wang,Tzu %A Huang,Yen-Ming %A Chan,Hsun-Yu %+ Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, No.33, Linsen S Rd, Zhongzheng Dist, Taipei City, 100025, Taiwan, 886 33668784, yenming927@ntu.edu.tw %K Asia %K adherence %K application %K feature %K medication %K mobile %D 2024 %7 8.11.2024 %9 Review %J J Med Internet Res %G English %X Background: Medication is crucial for managing chronic diseases, yet adherence rates are often suboptimal. With advanced integration of IT and mobile internet into health care, mobile apps present a substantial opportunity for improving adherence by incorporating personalized educational, behavioral, and organizational strategies. However, determining the most effective features and functionalities for these apps within the specific health care context in Asia remains a challenge. Objective: We aimed to review the existing literature, focusing on Asian countries, to identify the optimal features of mobile apps that can effectively enhance medication adherence within the unique context of Asian societies. Methods: We conducted a narrative review with the SPIDER (sample, phenomenon of interest, design, evaluation, research type) tool. We identified studies on mobile apps for medication adherence from January 2019 to August 2024 on PubMed and Scopus. Key search terms included “Asia,” “chronic disease,” “app,” “application,” “survey,” “experiment,” “questionnaire,” “group,” “medical adherence,” “medication adherence,” “case-control,” “cohort study,” “randomized controlled trial,” “clinical trial,” “observational study,” “qualitative research,” “mixed methods,” and “analysis,” combined using logical operators “OR” and “AND.” The features of mobile apps identified in the studies were evaluated, compared, and summarized based on their disease focuses, developers, target users, features, usability, and use. Results: The study identified 14 mobile apps designed to enhance medication adherence. Of these, 11 were developed by research teams, while 3 were created by commercial companies or hospitals. All the apps incorporated multiple features to support adherence, with reminders being the most common, present in 11 apps. Patient community forums were the least common, appearing in only 1 app. In total, 6 apps provided lifestyle modification functions, offering dietary and exercise recommendations, generating individualized plans, and monitoring progress. In addition, 6 apps featured health data recording and monitoring functions, with 4 allowing users to export and share records with researchers or health care professionals. Many apps included communication features, with 10 enabling feedback from researchers or health care professionals and 7 offering web-based consultation services. Educational content was available in 8 apps, and 7 used motivation strategies to encourage adherence. Six studies showed that mobile apps improved clinical outcomes, such as blood glucose, lipid, and pressure, while reducing adverse events and boosting physical activities. Twelve studies noted positive humanistic effects, including better medication adherence, quality of life, and user satisfaction. Conclusions: This review has identified key components integrated into mobile apps to support medication adherence. However, the lack of government and corporate involvement in their development limits the generalizability of any individual app. Beyond basic reminder functions, features such as multiuser support, feedback mechanisms, web-based consultations, motivational tools, and socialization features hold significant promise for improving medication adherence. Further pragmatic research is necessary to validate the effectiveness of these selected apps in enhancing adherence. %M 39514859 %R 10.2196/60787 %U https://www.jmir.org/2024/1/e60787 %U https://doi.org/10.2196/60787 %U http://www.ncbi.nlm.nih.gov/pubmed/39514859 %0 Journal Article %@ 2817-092X %I JMIR Publications %V 3 %N %P e59556 %T Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and Biological Breakthroughs %A Gutman,Barak %A Shmilovitch,Amit-Haim %A Aran,Dvir %A Shelly,Shahar %+ Department of Neurology, Rambam Medical Center, HaAliya HaShniya St 8, Haifa, 3109601, Israel, 972 4 777 3568, shahar.shell@technion.ac.il %K neurology %K artificial intelligence %K telemedicine %K clinical advancements %K mobile phone %D 2024 %7 8.11.2024 %9 Viewpoint %J JMIR Neurotech %G English %X Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with the burden of chronic neurodegenerative diseases having doubled over the last 2 decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence (AI) and bioinformatic methods is changing this landscape. This paper explores this transformative journey, emphasizing the critical role of AI in neurology, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, integrating biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. In addition, telemedicine and web-based interactive health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions such as multiple sclerosis, Parkinson disease, amyotrophic lateral sclerosis, Alzheimer disease, and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of IT and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this paper, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in AI will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric health care. %R 10.2196/59556 %U https://neuro.jmir.org/2024/1/e59556 %U https://doi.org/10.2196/59556 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e64950 %T Efficacy and Safety of a Therapy Combining Sintilimab and Chemotherapy With Cryoablation in the First-Line Treatment of Advanced Nonsquamous Non–Small Cell Lung Cancer: Protocol for a Phase II, Pilot, Single-Arm, Single-Center Study %A Gao,Zhiqiang %A Teng,Jiajun %A Qiao,Rong %A Qian,Jialin %A Pan,Feng %A Ma,Meili %A Lu,Jun %A Zhang,Bo %A Chu,Tianqing %A Zhong,Hua %+ Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 Huaihai West Road, Xuhui District, Shanghai, China, 86 02122200000, zhonghua_gcp@163.com %K cryoablation %K immunotherapy %K nonsquamous non–small cell lung cancer %D 2024 %7 8.11.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Immunotherapy has significantly advanced lung cancer treatment, particularly in nonsquamous non–small cell lung cancer (NSCLC), with overall response rates between 50% and 60%. However, about 30% of patients only achieve a stable disease state. Cryoablation has shown potential to enhance immunotherapy by modifying the tumor’s immune microenvironment through the release of antigens and immune factors. Addressing how to boost the immune response in these patients is critical. Objective: This study aims to investigate the efficacy and safety of immunochemotherapy in combination with cryoablation as a first-line treatment for advanced NSCLC. Methods: This is a phase II, pilot, open-label, single arm, single center, interventional study. Patients with stage IIIB to IIIC or IV NSCLC with T staging ranging from T1 to T2b will receive sintilimab (200 mg/m2 every 3 weeks) and chemotherapy. After 2 cycles, the feasibility of cryoablation will be considered for those with stable disease by a multidisciplinary team. Cryoablation with 3 freeze-thaw cycles will be performed for the main lesion. The third cycle of systemic therapy will begin 7 (SD 3) days after cryoablation. A total of 20 patients will be enrolled. Treatment will continue until the disease progresses, there is unacceptable toxicity, a participant withdraws consent, other discontinuation criteria are met, or the study reaches completion. The primary objective is to assess progression-free survival (PFS). The secondary objective is to assess efficacy through duration of response, disease control rate, overall survival (OS), and the safety profile. The exploratory objective is to investigate and compare immune factor changes after 2 cycles of immunochemotherapy and at 1, 3, and 7 days after cryoablation. Survival time will be estimated using the Kaplan-Meier method to calculate median PFS and OS. Any adverse events that occur during the trial will be promptly recorded. Results: The project was funded in 2024, and enrollment will be completed in 2025. The first results are expected to be submitted for publication in 2027. Conclusions: This study will provide evidence for the efficacy and safety of the combination of immunochemotherapy and cryoablation as a first-line treatment for advanced NSCLC. Although it has a limited sample size, the findings of this study will be used in the future to inform the design of a fully powered, 2-arm, larger-scale study. Trial Registration: ClinicalTrials.gov NCT06483009; https://clinicaltrials.gov/study/NCT06483009 International Registered Report Identifier (IRRID): PRR1-10.2196/64950 %M 39514267 %R 10.2196/64950 %U https://www.researchprotocols.org/2024/1/e64950 %U https://doi.org/10.2196/64950 %U http://www.ncbi.nlm.nih.gov/pubmed/39514267 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58039 %T Pioneering Klebsiella Pneumoniae Antibiotic Resistance Prediction With Artificial Intelligence-Clinical Decision Support System–Enhanced Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry: Retrospective Study %A Jian,Ming-Jr %A Lin,Tai-Han %A Chung,Hsing-Yi %A Chang,Chih-Kai %A Perng,Cherng-Lih %A Chang,Feng-Yee %A Shang,Hung-Sheng %+ Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Division of Clinical Pathology, Taipei City, 11490, Taiwan, 886 920713130, iamkeith001@gmail.com %K Klebsiella pneumoniae %K multidrug resistance %K AI-CDSS %K quinolone %K ciprofloxacin %K levofloxacin %D 2024 %7 7.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The rising prevalence and swift spread of multidrug-resistant gram-negative bacteria (MDR-GNB), especially Klebsiella pneumoniae (KP), present a critical global health threat highlighted by the World Health Organization, with mortality rates soaring approximately 50% with inappropriate antimicrobial treatment. Objective: This study aims to advance a novel strategy to develop an artificial intelligence-clinical decision support system (AI-CDSS) that combines machine learning (ML) with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), aiming to significantly improve the accuracy and speed of diagnosing antibiotic resistance, directly addressing the grave health risks posed by the widespread dissemination of pan drug-resistant gram-negative bacteria across numerous countries. Methods: A comprehensive dataset comprising 165,299 bacterial specimens and 11,996 KP isolates was meticulously analyzed using MALDI-TOF MS technology. Advanced ML algorithms were harnessed to sculpt predictive models that ascertain resistance to quintessential antibiotics, particularly levofloxacin and ciprofloxacin, by using the amassed spectral data. Results: Our ML models revealed remarkable proficiency in forecasting antibiotic resistance, with the random forest classifier emerging as particularly effective in predicting resistance to both levofloxacin and ciprofloxacin, achieving the highest area under the curve of 0.95. Performance metrics across different models, including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score, were detailed, underlining the potential of these algorithms in aiding the development of precision treatment strategies. Conclusions: This investigation highlights the synergy between MALDI-TOF MS and ML as a beacon of hope against the escalating threat of antibiotic resistance. The advent of AI-CDSS heralds a new era in clinical diagnostics, promising a future in which rapid and accurate resistance prediction becomes a cornerstone in combating infectious diseases. Through this innovative approach, we answered the challenge posed by KP and other multidrug-resistant pathogens, marking a significant milestone in our journey toward global health security. %M 39509693 %R 10.2196/58039 %U https://www.jmir.org/2024/1/e58039 %U https://doi.org/10.2196/58039 %U http://www.ncbi.nlm.nih.gov/pubmed/39509693 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e62877 %T Noninvasive, Multimodal Inflammatory Biomarker Discovery for Systemic Inflammation (NOVA Study): Protocol for a Cross-Sectional Study %A Shim,Jinjoo %A Muraru,Sinziana %A Dobrota,Rucsandra %A Fleisch,Elgar %A Distler,Oliver %A Barata,Filipe %+ Centre for Digital Health Interventions, ETH Zurich, Weinbergstrasse 56/58, Zurich, 8006, Switzerland, 41 765457890, jshim@ethz.ch %K systemic inflammation %K chronic inflammation %K inflammatory biomarkers %K biofluids %K serum %K urine %K sweat %K saliva %K exhaled breath %K stool %K C-reactive protein %K interleukin %K IL-1β %K IL-6 %K IL-8 %K IL-10 %K tumor necrosis factor %K TNF-α %K fractional exhaled nitric oxide %K calprotectin %K core body temperature %K noninvasive biomarker %K multimodal biomarker %K remote monitoring %K surrogate biomarker %K rheumatology %K chronic inflammatory disease %D 2024 %7 5.11.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Prolonged systemic inflammation is recognized as a major contributor to the development of various chronic inflammatory diseases. Daily measurements of inflammatory biomarkers can significantly improve disease monitoring of systemic inflammation, thus contributing to reducing the burden on patients and the health care system. There exists, however, no scalable, cost-efficient, and noninvasive biomarker for remote assessment of systemic inflammation. To this end, we propose a novel, multimodal, and noninvasive approach for measuring inflammatory biomarkers. Objective: This study aimed to evaluate the relationship between the levels of inflammatory biomarkers in serum (gold standard) and those measured noninvasively in urine, sweat, saliva, exhaled breath, stool, and core body temperature in patients with systemic inflammation. Methods: This study is a single-center, cross-sectional study and includes a total of 20 participants (10 patients with systemic inflammation and 10 control patients). Eligible participants provide serum, urine, sweat, saliva, exhaled breath, and stool samples for biomarker analyses. Core body temperature is measured using a sensor. The primary end point is the level of C-reactive protein (CRP). The secondary end points are interleukin (IL)–1β, IL-6, IL-8, IL-10, and tumor necrosis factor-α levels. The tertiary end points are fractional exhaled nitric oxide, calprotectin, and core body temperature. Samples will be collected in 2 batches, enabling preliminary analysis of the first batch (patients 1-5 from each group). The full analysis will include both batches. CRP and cytokine levels will be measured using enzyme-linked immunosorbent assay and electrochemiluminescence immunoassay. For statistical analysis, the Shapiro-Wilk test will be used to evaluate the normality of the distribution in each variable. We will perform the 2-tailed t test or Wilcoxon rank sum test to compare the levels of inflammatory biomarkers between patients with systemic inflammations and control patients. Pearson and Spearman correlation coefficients will assess the relationship between inflammatory biomarkers from noninvasive methods and serum biomarkers. Using all-subset regression analysis, we will determine the combination of noninvasive methods yielding the highest predictive accuracy for serum CRP levels. Participants’ preferences for sampling methods will be assessed through a questionnaire. Results: The study received ethics approval from the independent research ethics committee of Canton Zurich on October 28, 2022. A total of 20 participants participated in the study measurements. Data collection started on February 22, 2023, and was completed on September 22, 2023. Participants were on average 52.8 (SD 14.4; range 24-82) years of age, and 70% (14/20) of them were women. The analysis results reporting findings are expected to be published in 2025. Conclusions: This study aims to evaluate the feasibility of noninvasive, multimodal assessment of inflammatory biomarkers in patients with systemic inflammation. Promising results could lead to the creation of noninvasive and potentially digital biomarkers for systemic inflammation, enabling continuous monitoring and early diagnosis of inflammatory activity in a remote setting. International Registered Report Identifier (IRRID): DERR1-10.2196/62877 %M 39499914 %R 10.2196/62877 %U https://www.researchprotocols.org/2024/1/e62877 %U https://doi.org/10.2196/62877 %U http://www.ncbi.nlm.nih.gov/pubmed/39499914 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54528 %T Evaluating the Implementation and Clinical Effectiveness of an Innovative Digital First Care Model for Behavioral Health Using the RE-AIM Framework: Quantitative Evaluation %A Nordberg,Samuel S %A Jaso-Yim,Brittany A %A Sah,Pratha %A Schuler,Keke %A Eyllon,Mara %A Pennine,Mariesa %A Hoyler,Georgia H %A Barnes,J Ben %A Murillo,Lily Hong %A O'Dea,Heather %A Orth,Laura %A Rogers,Elizabeth %A Welch,George %A Peloquin,Gabrielle %A Youn,Soo Jeong %+ Reliant Medical Group, OptumCare, 5 Neponset St., Worcester, MA, 01606, United States, 1 5088560732, Samuel.Nordberg@reliantmedicalgroup.org %K digital mental health interventions %K implementation %K clinical effectiveness %K practice-oriented research %K access to care %D 2024 %7 30.10.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: In the United States, innovation is needed to address the increasing need for mental health care services and widen the patient-to-provider ratio. Despite the benefits of digital mental health interventions (DMHIs), they have not been effective in addressing patients’ behavioral health challenges as stand-alone treatments. Objective: This study evaluates the implementation and effectiveness of precision behavioral health (PBH), a digital-first behavioral health care model embedded within routine primary care that refers patients to an ecosystem of evidence-based DMHIs with strategically placed human support. Methods: Patient demographic information, triage visit outcomes, multidimensional patient-reported outcome measure, enrollment, and engagement with the DMHIs were analyzed using data from the electronic health record and vendor-reported data files. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework was used to evaluate the implementation and clinical effectiveness outcomes of PBH. Results: PBH had a 47.58% reach rate, defined as patients accepting the PBH referral from their behavioral health integrated clinician. PBH patients had high DMHI registration rates (79.62%), high activation rates (76.54%), and high retention rates at 15 days (57.69%) and 30 days (44.58%) compared to literature benchmarks. In total, 74.01% (n=168) of patients showed clinical improvement, 22.47% (n=51) showed no clinical change, and 3.52% (n=8) showed clinical deterioration in symptoms. PBH had high adoption rates, with behavioral health integrated clinicians referring on average 4.35 (SD 0.46) patients to PBH per month and 90%-100% of clinicians (n=12) consistently referring at least 1 patient to PBH each month. A third (32%, n=1114) of patients were offered PBH as a treatment option during their triage visit. Conclusions: PBH as a care model with evidence-based DMHIs, human support for patients, and integration within routine settings offers a credible service to support patients with mild to moderate mental health challenges. This type of model has the potential to address real-life access to care problems faced by health care settings. %M 39476366 %R 10.2196/54528 %U https://www.jmir.org/2024/1/e54528 %U https://doi.org/10.2196/54528 %U http://www.ncbi.nlm.nih.gov/pubmed/39476366 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e58439 %T Internet-Based Abnormal Chromosomal Diagnosis During Pregnancy Using a Noninvasive Innovative Approach to Detecting Chromosomal Abnormalities in the Fetus: Scoping Review %A Oyovwi,Mega Obukohwo Sr %A Ohwin,Ejiro Peggy %A Rotu,Rume Arientare %A Olowe,Temitope Gideon %+ Department of Physiology, Adeleke University, Ede, Osun State, Ede, 33105, Nigeria, 234 8066096369, megalect@gmail.com %K internet-based %K abnormal chromosomal diagnosis %K pregnancy %K noninvasive %K innovative approach %K detecting %K preventing %K chromosomal abnormalities %K fetus %D 2024 %7 16.10.2024 %9 Review %J JMIR Bioinform Biotech %G English %X Background: Chromosomal abnormalities are genetic disorders caused by chromosome errors, leading to developmental delays, birth defects, and miscarriages. Currently, invasive procedures such as amniocentesis or chorionic villus sampling are mostly used, which carry a risk of miscarriage. This has led to the need for a noninvasive and innovative approach to detect and prevent chromosomal abnormalities during pregnancy. Objective: This review aims to describe and appraise the potential of internet-based abnormal chromosomal preventive measures as a noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. Methods: A thorough review of existing literature and research on chromosomal abnormalities and noninvasive approaches to prenatal diagnosis and therapy was conducted. Electronic databases such as PubMed, Google Scholar, ScienceDirect, CENTRAL, CINAHL, Embase, OVID MEDLINE, OVID PsycINFO, Scopus, ACM, and IEEE Xplore were searched for relevant studies and articles published in the last 5 years. The keywords used included chromosomal abnormalities, prenatal diagnosis, noninvasive, and internet-based, and diagnosis. Results: The review of literature revealed that internet-based abnormal chromosomal diagnosis is a potential noninvasive approach to detecting and preventing chromosomal abnormalities during pregnancy. This innovative approach involves the use of advanced technology, including high-resolution ultrasound, cell-free DNA testing, and bioinformatics, to analyze fetal DNA from maternal blood samples. It allows early detection of chromosomal abnormalities, enabling timely interventions and treatment to prevent adverse outcomes. Furthermore, with the advancement of technology, internet-based abnormal chromosomal diagnosis has emerged as a safe alternative with benefits including its cost-effectiveness, increased accessibility and convenience, potential for earlier detection and intervention, and ethical considerations. Conclusions: Internet-based abnormal chromosomal diagnosis has the potential to revolutionize prenatal care by offering a safe and noninvasive alternative to invasive procedures. It has the potential to improve the detection of chromosomal abnormalities, leading to better pregnancy outcomes and reduced risk of miscarriage. Further research and development in this field is needed to make this approach more accessible and affordable for pregnant women. %M 39412876 %R 10.2196/58439 %U https://bioinform.jmir.org/2024/1/e58439 %U https://doi.org/10.2196/58439 %U http://www.ncbi.nlm.nih.gov/pubmed/39412876 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e62667 %T Effect of Semaglutide on Physical Function, Body Composition, and Biomarkers of Aging in Older Adults With Overweight and Insulin Resistance: Protocol for an Open-Labeled Randomized Controlled Trial %A Cortes,Tiffany M %A Vasquez,Libia %A Serra,Monica C %A Robbins,Ronna %A Stepanenko,Allison %A Brown,Kevin %A Barrus,Hannah %A Campos,Annalisa %A Espinoza,Sara E %A Musi,Nicolas %+ Division of Endocrinology, Department of Medicine, University of Texas Health Science Center San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, United States, 1 210 949 9759, cortest@uthscsa.edu %K glucagon-like peptide %K lean body mass %K physical function %K biomarkers of aging %K semaglutide %D 2024 %7 13.9.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Older adults with type 2 diabetes mellitus (T2DM) or prediabetes are at increased risk of adverse changes in body composition, physical function, and aging-related biomarkers compared to those with normal glucose tolerance. Semaglutide is a glucagon-like peptide 1 receptor agonist that has been approved for T2DM and chronic weight management. Although semaglutide is effective for weight loss and T2DM management, its effects on lean body mass, physical function, and biomarkers of aging are understudied in older adults. Objective: This study aims to compare the effects of lifestyle counseling with and that without semaglutide on body composition, physical function, and biomarkers of aging in older adults. Methods: This is an open-label randomized controlled trial. A total of 20 adults (aged 65 years and older) with elevated BMI (27-40 kg/m2) and prediabetes or well-controlled T2DM (hemoglobin A1c 5.7%-7.5%) are recruited, stratified by sex, and randomized 1:1 to one of 2 groups (semaglutide plus lifestyle counseling vs lifestyle counseling alone) and followed up for 5 months. Those in the semaglutide group are titrated to 1 mg weekly, as tolerated, for 12 weeks. Lifestyle counseling is given by registered dietitians and based on the Diabetes Prevention Program Lifestyle Change Program. Our primary outcomes include changes in lean mass, physical function, and biomarkers of aging. Body composition is measured by dual-energy x-ray absorptiometry and includes total fat mass and lean mass. Physical function is measured by 6-minute walk distance, grip strength, and short physical performance battery. Biomarkers of aging are measured in blood, skeletal muscle, and abdominal adipose tissue to include C-reactive protein, interleukin-6, tumor necrosis factors α, and β galactosidase staining. Results: The study was funded in December 2021 with a projected data collection period from spring 2023 through summer 2024. Conclusions: Despite the elevated risk of adverse changes in body composition, physical function, and biomarkers of aging among older adults with glucose intolerance and elevated adiposity, the benefits and risks of commonly prescribed antihyperglycemic or weight loss medications such as semaglutide are understudied. This study aims to fill this knowledge gap to inform clinicians about the potential for additional clinically meaningful, nonglycemic effects of semaglutide. Trial Registration: ClinicalTrials.gov NCT05786521; https://clinicaltrials.gov/study/NCT05786521 International Registered Report Identifier (IRRID): DERR1-10.2196/62667 %M 39269759 %R 10.2196/62667 %U https://www.researchprotocols.org/2024/1/e62667 %U https://doi.org/10.2196/62667 %U http://www.ncbi.nlm.nih.gov/pubmed/39269759 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59826 %T Beyond Step Count: Are We Ready to Use Digital Phenotyping to Make Actionable Individual Predictions in Psychiatry? %A Ortiz,Abigail %A Mulsant,Benoit H %+ Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 8th Floor, 250 College Street, Toronto, ON, M5T 1R8, Canada, 1 416 979 6948, benoit.mulsant@utoronto.ca %K digital phenotype %K digital phenotyping %K prediction %K predictions %K mental health %K mental illness %K mental illnesses %K mental disorder %K mental disorders %K US National Institute of Mental Health %K NIMH %K psychiatry %K psychiatrist %K psychiatrists %D 2024 %7 5.8.2024 %9 Viewpoint %J J Med Internet Res %G English %X Some models for mental disorders or behaviors (eg, suicide) have been successfully developed, allowing predictions at the population level. However, current demographic and clinical variables are neither sensitive nor specific enough for making individual actionable clinical predictions. A major hope of the “Decade of the Brain” was that biological measures (biomarkers) would solve these issues and lead to precision psychiatry. However, as models are based on sociodemographic and clinical data, even when these biomarkers differ significantly between groups of patients and control participants, they are still neither sensitive nor specific enough to be applied to individual patients. Technological advances over the past decade offer a promising approach based on new measures that may be essential for understanding mental disorders and predicting their trajectories. Several new tools allow us to continuously monitor objective behavioral measures (eg, hours of sleep) and densely sample subjective measures (eg, mood). The promise of this approach, referred to as digital phenotyping, was recognized almost a decade ago, with its potential impact on psychiatry being compared to the impact of the microscope on biological sciences. However, despite the intuitive belief that collecting densely sampled data (big data) improves clinical outcomes, recent clinical trials have not shown that incorporating digital phenotyping improves clinical outcomes. This viewpoint provides a stepwise development and implementation approach, similar to the one that has been successful in the prediction and prevention of cardiovascular disease, to achieve clinically actionable predictions in psychiatry. %M 39102686 %R 10.2196/59826 %U https://www.jmir.org/2024/1/e59826 %U https://doi.org/10.2196/59826 %U http://www.ncbi.nlm.nih.gov/pubmed/39102686 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e49230 %T Pharmacogenetics Clinical Decision Support Systems for Primary Care in England: Co-Design Study %A Sharma,Videha %A McDermott,John %A Keen,Jessica %A Foster,Simon %A Whelan,Pauline %A Newman,William %+ Centre for Health Informatics, Division of Informatics, Imaging and Data Science, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 7735360958, videha.sharma@manchester.ac.uk %K personalized medicine %K genomic medicine %K pharmacogenetics %K user-centred design %K medical informatics %K clinical decision support systems %K side effect %K information technology %K data %K primary care %K health informatic %D 2024 %7 23.7.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Pharmacogenetics can impact patient care and outcomes through personalizing the selection of medicines, resulting in improved efficacy and a reduction in harmful side effects. Despite the existence of compelling clinical evidence and international guidelines highlighting the benefits of pharmacogenetics in clinical practice, implementation within the National Health Service in the United Kingdom is limited. An important barrier to overcome is the development of IT solutions that support the integration of pharmacogenetic data into health care systems. This necessitates a better understanding of the role of electronic health records (EHRs) and the design of clinical decision support systems that are acceptable to clinicians, particularly those in primary care. Objective: Explore the needs and requirements of a pharmacogenetic service from the perspective of primary care clinicians with a view to co-design a prototype solution. Methods: We used ethnographic and think-aloud observations, user research workshops, and prototyping. The participants for this study included general practitioners and pharmacists. In total, we undertook 5 sessions of ethnographic observation to understand current practices and workflows. This was followed by 3 user research workshops, each with its own topic guide starting with personas and early ideation, through to exploring the potential of clinical decision support systems and prototype design. We subsequently analyzed workshop data using affinity diagramming and refined the key requirements for the solution collaboratively as a multidisciplinary project team. Results: User research results identified that pharmacogenetic data must be incorporated within existing EHRs rather than through a stand-alone portal. The information presented through clinical decision support systems must be clear, accessible, and user-friendly as the service will be used by a range of end users. Critically, the information should be displayed within the prescribing workflow, rather than discrete results stored statically in the EHR. Finally, the prescribing recommendations should be authoritative to provide confidence in the validity of the results. Based on these findings we co-designed an interactive prototype, demonstrating pharmacogenetic clinical decision support integrated within the prescribing workflow of an EHR. Conclusions: This study marks a significant step forward in the design of systems that support pharmacogenetic-guided prescribing in primary care settings. Clinical decision support systems have the potential to enhance the personalization of medicines, provided they are effectively implemented within EHRs and present pharmacogenetic data in a user-friendly, actionable, and standardized format. Achieving this requires the development of a decoupled, standards-based architecture that allows for the separation of data from application, facilitating integration across various EHRs through the use of application programming interfaces (APIs). More globally, this study demonstrates the role of health informatics and user-centered design in realizing the potential of personalized medicine at scale and ensuring that the benefits of genomic innovation reach patients and populations effectively. %M 39042886 %R 10.2196/49230 %U https://www.jmir.org/2024/1/e49230 %U https://doi.org/10.2196/49230 %U http://www.ncbi.nlm.nih.gov/pubmed/39042886 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e53396 %T Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review %A AlSaad,Rawan %A Abd-alrazaq,Alaa %A Choucair,Fadi %A Ahmed,Arfan %A Aziz,Sarah %A Sheikh,Javaid %+ AI Center for Precision Health, Weill Cornell Medicine-Qatar, Education City, Street 2700, Doha, Qatar, 974 44928830, rta4003@qatar-med.cornell.edu %K artificial intelligence %K AI %K AI models %K AI model %K in vitro fertilization %K IVF %K ovarian stimulation %K infertility %K fertility %K ovary %K ovaries %K reproductive %K reproduction %K gynecology %K prediction %K predictions %K predictive %K prediction model %K ovarian %K adverse outcome %K fertilization %K pregnancy %D 2024 %7 5.7.2024 %9 Review %J J Med Internet Res %G English %X Background: In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. Objective: The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. Methods: A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. Results: Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. Conclusions: These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates. %M 38967964 %R 10.2196/53396 %U https://www.jmir.org/2024/1/e53396 %U https://doi.org/10.2196/53396 %U http://www.ncbi.nlm.nih.gov/pubmed/38967964 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54265 %T Making Science Computable Using Evidence-Based Medicine on Fast Healthcare Interoperability Resources: Standards Development Project %A Soares,Andrey %A Schilling,Lisa M %A Richardson,Joshua %A Kommadi,Bhagvan %A Subbian,Vignesh %A Dehnbostel,Joanne %A Shahin,Khalid %A Robinson,Karen A %A Afzal,Muhammad %A Lehmann,Harold P %A Kunnamo,Ilkka %A Alper,Brian S %+ Department of Medicine, University of Colorado Anschutz Medical Campus, 1890 North Revere Court, Mailstop F443, Aurora, CO, 80045, United States, 1 3037242825, andrey.soares@cuanschutz.edu %K evidence-based medicine %K FHIR %K Fast Healthcare Interoperability Resources %K computable evidence %K EBMonFHIR %K evidence-based medicine on Fast Healthcare Interoperability Resources %D 2024 %7 25.6.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy. Objective: This study aimed to introduce the EBM on Fast Healthcare Interoperability Resources (FHIR) project (EBMonFHIR), which is extending the methods and infrastructure of Health Level Seven (HL7) FHIR to provide an interoperability standard for the electronic exchange of health-related scientific knowledge. Methods: As an ongoing process, the project creates and refines FHIR resources to represent evidence from clinical studies and syntheses of those studies and develops tools to assist with the creation and visualization of FHIR resources. Results: The EBMonFHIR project created FHIR resources (ie, ArtifactAssessment, Citation, Evidence, EvidenceReport, and EvidenceVariable) for representing evidence. The COVID-19 Knowledge Accelerator (COKA) project, now Health Evidence Knowledge Accelerator (HEvKA), took this work further and created FHIR resources that express EvidenceReport, Citation, and ArtifactAssessment concepts. The group is (1) continually refining FHIR resources to support the representation of EBM; (2) developing controlled terminology related to EBM (ie, study design, statistic type, statistical model, and risk of bias); and (3) developing tools to facilitate the visualization and data entry of EBM information into FHIR resources, including human-readable interfaces and JSON viewers. Conclusions: EBMonFHIR resources in conjunction with other FHIR resources can support relaying EBM components in a manner that is interoperable and consumable by downstream tools and health information technology systems to support the users of evidence. %M 38916936 %R 10.2196/54265 %U https://www.jmir.org/2024/1/e54265 %U https://doi.org/10.2196/54265 %U http://www.ncbi.nlm.nih.gov/pubmed/38916936 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e55632 %T It Is in Our DNA: Bringing Electronic Health Records and Genomic Data Together for Precision Medicine %A Robertson,Alan J %A Mallett,Andrew J %A Stark,Zornitza %A Sullivan,Clair %+ Queensland Digital Health Centre, University of Queensland, Health Sciences Building, Herston Campus, Royal Brisbane and Women's Hospital, Brisbane, 4029, Australia, 61 733465343, c.sullivan1@uq.edu.au %K genomics %K digital health %K genetics %K precision medicine %K genomic %K genomic data %K electronic health records %K DNA %K supports %K decision-making %K timeliness %K diagnosis %K risk reduction %K electronic medical records %D 2024 %7 13.6.2024 %9 Viewpoint %J JMIR Bioinform Biotech %G English %X Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine. %M 38935958 %R 10.2196/55632 %U https://bioinform.jmir.org/2024/1/e55632 %U https://doi.org/10.2196/55632 %U http://www.ncbi.nlm.nih.gov/pubmed/38935958 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e56271 %T Defining and Risk-Stratifying Immunosuppression (the DESTINIES Study): Protocol for an Electronic Delphi Study %A Leston,Meredith %A Ordóñez-Mena,José %A Joy,Mark %A de Lusignan,Simon %A Hobbs,Richard %A McInnes,Iain %A Lee,Lennard %+ Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Primary Care Building, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 7896980320, meredith.leston@phc.ox.ac.uk %K immunosuppressed %K immunocompromised %K COVID %K vaccines %K COVID-19 %K surveillance %K phenotype %K adult %K immunosuppression %K clinical risk %K disease surveillance %K clinical consensus %K eDelphi %K immunosuppressed patient %K immunosuppressed patients %K study design %K Delphi %K methods %K methodology %K statistic %K statistics %K statistical %K consensus %K immune %K immunity %K immunology %K immunological %D 2024 %7 6.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Globally, there are marked inconsistencies in how immunosuppression is characterized and subdivided into clinical risk groups. This is detrimental to the precision and comparability of disease surveillance efforts—which has negative implications for the care of those who are immunosuppressed and their health outcomes. This was particularly apparent during the COVID-19 pandemic; despite collective motivation to protect these patients, conflicting clinical definitions created international rifts in how those who were immunosuppressed were monitored and managed during this period. We propose that international clinical consensus be built around the conditions that lead to immunosuppression and their gradations of severity concerning COVID-19. Such information can then be formalized into a digital phenotype to enhance disease surveillance and provide much-needed intelligence on risk-prioritizing these patients. Objective: We aim to demonstrate how electronic Delphi objectives, methodology, and statistical approaches will help address this lack of consensus internationally and deliver a COVID-19 risk-stratified phenotype for “adult immunosuppression.” Methods: Leveraging existing evidence for heterogeneous COVID-19 outcomes in adults who are immunosuppressed, this work will recruit over 50 world-leading clinical, research, or policy experts in the area of immunology or clinical risk prioritization. After 2 rounds of clinical consensus building and 1 round of concluding debate, these panelists will confirm the medical conditions that should be classed as immunosuppressed and their differential vulnerability to COVID-19. Consensus statements on the time and dose dependencies of these risks will also be presented. This work will be conducted iteratively, with opportunities for panelists to ask clarifying questions between rounds and provide ongoing feedback to improve questionnaire items. Statistical analysis will focus on levels of agreement between responses. Results: This protocol outlines a robust method for improving consensus on the definition and meaningful subdivision of adult immunosuppression concerning COVID-19. Panelist recruitment took place between April and May of 2024; the target set for over 50 panelists was achieved. The study launched at the end of May and data collection is projected to end in July 2024. Conclusions: This protocol, if fully implemented, will deliver a universally acceptable, clinically relevant, and electronic health record–compatible phenotype for adult immunosuppression. As well as having immediate value for COVID-19 resource prioritization, this exercise and its output hold prospective value for clinical decision-making across all diseases that disproportionately affect those who are immunosuppressed. International Registered Report Identifier (IRRID): PRR1-10.2196/56271 %M 38842925 %R 10.2196/56271 %U https://www.researchprotocols.org/2024/1/e56271 %U https://doi.org/10.2196/56271 %U http://www.ncbi.nlm.nih.gov/pubmed/38842925 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e54332 %T Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review %A Thomas,Mara %A Mackes,Nuria %A Preuss-Dodhy,Asad %A Wieland,Thomas %A Bundschus,Markus %+ F. Hoffmann-La Roche AG, Grenzacherstrasse 124, Basel, 4070, Switzerland, 41 616881111, mara.thomas@roche.com %K genetic privacy %K privacy %K data anonymization %K reidentification %D 2024 %7 27.5.2024 %9 Review %J JMIR Bioinform Biotech %G English %X Background: Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation. Objective: This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets. Methods: We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success. Results: From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants. Conclusions: On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk. %M 38935957 %R 10.2196/54332 %U https://bioinform.jmir.org/2024/1/e54332 %U https://doi.org/10.2196/54332 %U http://www.ncbi.nlm.nih.gov/pubmed/38935957 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50204 %T Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins %A Vallée,Alexandre %+ Department of Epidemiology and Public Health, Foch Hospital, 40 rue Worth, Suresnes, 92150, France, 33 0146257352, al.vallee@hopital-foch.com %K digital health %K digital twin %K personalized medicine %K prevention %K prediction %K health care system %D 2024 %7 13.5.2024 %9 Viewpoint %J J Med Internet Res %G English %X Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care. %M 38739913 %R 10.2196/50204 %U https://www.jmir.org/2024/1/e50204 %U https://doi.org/10.2196/50204 %U http://www.ncbi.nlm.nih.gov/pubmed/38739913 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e54042 %T A Predictive Noninvasive Single-Nucleotide Variation–Based Biomarker Signature for Resectable Pancreatic Cancer: Protocol for a Prospective Validation Study %A Seeger,Nico %A Gutknecht,Stefan %A Zschokke,Irin %A Fleischmann,Isabella %A Roth,Nadja %A Metzger,Jürg %A Weber,Markus %A Breitenstein,Stefan %A Grochola,Lukasz Filip %+ Department of Visceral and Thoracic Surgery, Cantonal Hospital of Winterthur, Brauerstrasse 15, Winterthur, 8401, Switzerland, 41 52 266 41 62, lukaszfilip.grochola@ksw.ch %K single-nucleotide polymorphism %K SNP %K single-nucleotide variation %K SNV %K pancreatic ductal adenocarcinoma %K PDAC %K noninvasive biomarker %K survival %K resection %K prospective validation %D 2024 %7 13.5.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Single-nucleotide variations (SNVs; formerly SNPs) are inherited genetic variants that can be easily determined in routine clinical practice using a simple blood or saliva test. SNVs have potential to serve as noninvasive biomarkers for predicting cancer-specific patient outcomes after resection of pancreatic ductal adenocarcinoma (PDAC). Two recent analyses led to the identification and validation of three SNVs in the CD44 and CHI3L2 genes (rs187115, rs353630, and rs684559), which can be used as predictive biomarkers to help select patients most likely to benefit from pancreatic resection. These variants were associated with an over 2-fold increased risk for tumor-related death in three independent PDAC study cohorts from Europe and the United States, including The Cancer Genome Atlas cohorts (reaching a P value of 1×10–8). However, these analyses were limited by the inherent biases of a retrospective study design, such as selection and publication biases, thereby limiting the clinical use of these promising biomarkers in guiding PDAC therapy. Objective: To overcome the limitations of previous retrospectively designed studies and translate the findings into clinical practice, we aim to validate the association of the identified SNVs with survival in a controlled setting using a prospective cohort of patients with PDAC following pancreatic resection. Methods: All patients with PDAC who will undergo pancreatic resection at three participating hospitals in Switzerland and fulfill the inclusion criteria will be included in the study consecutively. The SNV genotypes will be determined using standard genotyping techniques from patient blood samples. For each genotyped locus, log-rank and Cox multivariate regression tests will be performed, accounting for the relevant covariates American Joint Committee on Cancer stage and resection status. Clinical follow-up data will be collected for at least 3 years. Sample size calculation resulted in a required sample of 150 patients to sufficiently power the analysis. Results: The follow-up data collection started in August 2019 and the estimated end of data collection will be in May 2027. The study is still recruiting participants and 142 patients have been recruited as of November 2023. The DNA extraction and genotyping of the SNVs will be performed after inclusion of the last patient. Since no SNV genotypes have been determined, no data analysis has been performed to date. The results are expected to be published in 2027. Conclusions: This is the first prospective study of the CD44 and CHI3L2 SNV–based biomarker signature in PDAC. A prospective validation of this signature would enable its clinical use as a noninvasive predictive biomarker of survival after pancreatic resection that is readily available at the time of diagnosis and can assist in guiding PDAC therapy. The results of this study may help to individualize treatment decisions and potentially improve patient outcomes. International Registered Report Identifier (IRRID): DERR1-10.2196/54042 %M 38635586 %R 10.2196/54042 %U https://www.researchprotocols.org/2024/1/e54042 %U https://doi.org/10.2196/54042 %U http://www.ncbi.nlm.nih.gov/pubmed/38635586 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e44948 %T Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study %A Wolff,Christian %A Steinheimer,Patrick %A Warmerdam,Elke %A Dahmen,Tim %A Slusallek,Philipp %A Schlinkmann,Christian %A Chen,Fei %A Orth,Marcel %A Pohlemann,Tim %A Ganse,Bergita %+ Innovative Implant Development (Fracture Healing), Departments and Institutes of Surgery, Saarland University, Kirrberger Straße 1, Building 57, Homburg/Saar, 66421, Germany, 49 684116 ext 31570, bergita.ganse@uks.eu %K podiatry %K podiatric medicine %K movement analysis %K ground reaction forces %K wearables %K slope %K gait analysis %K monitoring %K gait %K rehabilitation %K treatment %K sensor %K injury %K postoperative treatment %K sensors %K personalized medicine %K movement %K digital health %K pedography %K baropedography %D 2024 %7 8.5.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. Objective: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. Methods: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: −20%, −15%, −10%, −5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Results: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. Conclusions: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory. %M 38718385 %R 10.2196/44948 %U https://www.jmir.org/2024/1/e44948 %U https://doi.org/10.2196/44948 %U http://www.ncbi.nlm.nih.gov/pubmed/38718385 %0 Journal Article %@ 2563-3570 %I JMIR Publications %V 5 %N %P e56884 %T The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis %A Bui,Huong Thi Thu %A Nguyễn Thị Phương,Quỳnh %A Cam Tu,Ho %A Nguyen Phuong,Sinh %A Pham,Thuy Thi %A Vu,Thu %A Nguyen Thi Thu,Huyen %A Khanh Ho,Lam %A Nguyen Tien,Dung %+ Department of Internal Medicine, Thai Nguyen University of Medicine and Pharmacy, 284 Luong Ngoc Quyen, Quang Trung, Thai Nguyen, 250000, Vietnam, 84 913516863, dung.nt@tnmc.edu.vn %K Glasgow Coma Scale %K ischemic stroke %K hierarchical cluster analysis %K clustering %K machine learning %K MTHFR %K NOTCH3 %K modified Rankin scale %K National Institutes of Health Stroke Scale %K prothrombin %K thrombophilia %K mutations %K genetics %K genomics %K ischemia %K risk %K risk analysis %D 2024 %7 7.5.2024 %9 Original Paper %J JMIR Bioinform Biotech %G English %X Background: The etiology of ischemic stroke is multifactorial. Several gene mutations have been identified as leading causes of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a hereditary disease that causes stroke and other neurological symptoms. Objective: We aimed to identify the variants of NOTCH3 and thrombophilia genes, and their complex interactions with other factors. Methods: We conducted a hierarchical cluster analysis (HCA) on the data of 100 patients diagnosed with ischemic stroke. The variants of NOTCH3 and thrombophilia genes were identified by polymerase chain reaction with confronting 2-pair primers and real-time polymerase chain reaction. The overall preclinical characteristics, cumulative cutpoint values, and factors associated with these somatic mutations were analyzed in unidimensional and multidimensional scaling models. Results: We identified the following optimal cutpoints: creatinine, 83.67 (SD 9.19) µmol/L; age, 54 (SD 5) years; prothrombin (PT) time, 13.25 (SD 0.17) seconds; and international normalized ratio (INR), 1.02 (SD 0.03). Using the Nagelkerke method, cutpoint 50% values of the Glasgow Coma Scale score; modified Rankin scale score; and National Institutes of Health Stroke Scale scores at admission, after 24 hours, and at discharge were 12.77, 2.86 (SD 1.21), 9.83 (SD 2.85), 7.29 (SD 2.04), and 6.85 (SD 2.90), respectively. Conclusions: The variants of MTHFR (C677T and A1298C) and NOTCH3 p.R544C may influence the stroke severity under specific conditions of PT, creatinine, INR, and BMI, with risk ratios of 4.8 (95% CI 1.53-15.04) and 3.13 (95% CI 1.60-6.11), respectively (Pfisher<.05). It is interesting that although there are many genes linked to increased atrial fibrillation risk, not all of them are associated with ischemic stroke risk. With the detection of stroke risk loci, more information can be gained on their impacts and interconnections, especially in young patients. %M 38935968 %R 10.2196/56884 %U https://bioinform.jmir.org/2024/1/e56884 %U https://doi.org/10.2196/56884 %U http://www.ncbi.nlm.nih.gov/pubmed/38935968 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e50164 %T An Electronic Health Record–Integrated Application for Standardizing Care and Monitoring Patients With Autosomal Dominant Polycystic Kidney Disease Enrolled in a Tolvaptan Clinic: Design and Implementation Study %A Chedid,Maroun %A Chebib,Fouad T %A Dahlen,Erin %A Mueller,Theodore %A Schnell,Theresa %A Gay,Melissa %A Hommos,Musab %A Swaminathan,Sundararaman %A Garg,Arvind %A Mao,Michael %A Amberg,Brigid %A Balderes,Kirk %A Johnson,Karen F %A Bishop,Alyssa %A Vaughn,Jackqueline Kay %A Hogan,Marie %A Torres,Vicente %A Chaudhry,Rajeev %A Zoghby,Ziad %K ADPKD %K autosomal dominant polycystic kidney disease %K polycystic kidney disease %K tolvaptan %K EHR %K electronic health record %K digital health solutions %K monitoring %K kidney disease %K drug-related toxicity %K digital application %K management %K chronic disease %D 2024 %7 1.5.2024 %9 %J JMIR Med Inform %G English %X Background: Tolvaptan is the only US Food and Drug Administration–approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD), but it requires strict clinical monitoring due to potential serious adverse events. Objective: We aimed to share our experience in developing and implementing an electronic health record (EHR)–based application to monitor patients with ADPKD who were initiated on tolvaptan. Methods: The application was developed in collaboration with clinical informatics professionals based on our clinical protocol with frequent laboratory test monitoring to detect early drug-related toxicity. The application streamlined the clinical workflow and enabled our nursing team to take appropriate actions in real time to prevent drug-related serious adverse events. We retrospectively analyzed the characteristics of the enrolled patients. Results: As of September 2022, a total of 214 patients were enrolled in the tolvaptan program across all Mayo Clinic sites. Of these, 126 were enrolled in the Tolvaptan Monitoring Registry application and 88 in the Past Tolvaptan Patients application. The mean age at enrollment was 43.1 (SD 9.9) years. A total of 20 (9.3%) patients developed liver toxicity, but only 5 (2.3%) had to discontinue the drug. The 2 EHR-based applications allowed consolidation of all necessary patient information and real-time data management at the individual or population level. This approach facilitated efficient staff workflow, monitoring of drug-related adverse events, and timely prescription renewal. Conclusions: Our study highlights the feasibility of integrating digital applications into the EHR workflow to facilitate efficient and safe care delivery for patients enrolled in a tolvaptan program. This workflow needs further validation but could be extended to other health care systems managing chronic diseases requiring drug monitoring. %R 10.2196/50164 %U https://medinform.jmir.org/2024/1/e50164 %U https://doi.org/10.2196/50164 %0 Journal Article %@ 2561-1011 %I JMIR Publications %V 8 %N %P e53091 %T Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data %A Shara,Nawar %A Mirabal-Beltran,Roxanne %A Talmadge,Bethany %A Falah,Noor %A Ahmad,Maryam %A Dempers,Ramon %A Crovatt,Samantha %A Eisenberg,Steven %A Anderson,Kelley %+ School of Nursing, Georgetown University, 3700 Reservoir Road, NW, Washington, DC, 20057, United States, 1 2026873496, rm1910@georgetown.edu %K machine learning %K preeclampsia %K cardiovascular %K maternal %K obstetrics %K health disparities %K woman %K women %K pregnancy %K pregnant %K cardiovascular %K cardiovascular condition %K retrospective study %K electronic health record %K EHR %K technology %K decision-making %K health disparity %K virtual server %K thromboembolism %K kidney failure %K HOPE-CAT %D 2024 %7 22.4.2024 %9 Original Paper %J JMIR Cardio %G English %X Background: Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions. Objective: This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes. Methods: Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm’s learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR. Results: In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days. Conclusions: This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities. %M 38648629 %R 10.2196/53091 %U https://cardio.jmir.org/2024/1/e53091 %U https://doi.org/10.2196/53091 %U http://www.ncbi.nlm.nih.gov/pubmed/38648629 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55202 %T A Health Information Technology Protocol to Enhance Colorectal Cancer Screening %A Baus,Adam %A Boatman,Dannell D %A Calkins,Andrea %A Pollard,Cecil %A Conn,Mary Ellen %A Subramanian,Sujha %A Kennedy-Rea,Stephenie %+ Department of Social and Behavioral Sciences, School of Public Health, West Virginia University, 64 Medical Center Drive, PO Box 9190, Morgantown, WV, 26506, United States, 1 304 293 1083, abaus@hsc.wvu.edu %K electronic health record %K EHR %K colorectal cancer screening %K health information technology %K cancer %K colorectal cancer %D 2024 %7 19.4.2024 %9 Research Letter %J JMIR Form Res %G English %X This study addresses barriers to electronic health records–based colorectal cancer screening and follow-up in primary care through the development and implementation of a health information technology protocol. %M 38640474 %R 10.2196/55202 %U https://formative.jmir.org/2024/1/e55202 %U https://doi.org/10.2196/55202 %U http://www.ncbi.nlm.nih.gov/pubmed/38640474 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e54086 %T Carboplatin in Patients With Metastatic Castration-Resistant Prostate Cancer Harboring Somatic or Germline Homologous Recombination Repair Gene Mutations: Phase II Single-Arm Trial %A Jain,Rishabh %A Kumar,Akash %A Sharma,Atul %A Sahoo,Ranjit Kumar %A Sharma,Aparna %A Seth,Amlesh %A Nayak,Brusabhanu %A Shamim,Shamim A %A Kaushal,Seema %A KP,Haresh %A Das,Chandan J %A Batra,Atul %+ Department of Medical Oncology, 160D Dr BR Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, 110029, India, 91 11 29575043, batraatul85@gmail.com %K carboplatin %K mCRPC %K prostate cancer %K homologous recombinant gene repair %K metastatic castration-resistant prostate cancer %K incurable %K deleterious mutation %K synthetic lethality %K tumor %K DNA %K low-income %K middle-income %K chemotherapeutic %K drug %K retrospective study %K taxane %K novel antiandrogen %K single-arm study %K health-related %K quality of life %K bone lesion %D 2024 %7 18.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Approximately 20%-25% of patients with metastatic castration-resistant prostate cancer (mCRPC) harbor a deleterious germline or somatic mutation in the homologous recombination repair (HRR) pathway genes, which is involved in the repair of double-stranded DNA damage. Half of these mutations are germline, while the remaining are exclusively somatic. While polyadenosine 5’diphosphoribose [poly (ADP-ribose)] polymerase inhibitors, such as olaparib and rucaparib, are effective in this subgroup, their widespread use is limited due to the associated high cost, especially in resource-constrained settings. Notably, platinum agents like carboplatin have exquisite sensitivity to cells with defective DNA repair machinery. Carboplatin, a conventional, inexpensive chemotherapeutic agent, offers a potential alternative treatment in such patients. Several retrospective small case series support this hypothesis. However, there are no prospective clinical trials of carboplatin in patients with mCRPC with HRR mutations. Objective: The primary objective is to assess the objective response rate of 3 weekly carboplatin treatments in patients with mCRPC harboring deleterious mutations in the HRR pathway genes and previously treated with a taxane or a novel antiandrogen agent. The secondary objectives include progression-free survival, health-related quality of life, and safety profile of carboplatin. Methods: Patients diagnosed with mCRPC harboring HRR pathway mutations previously treated with docetaxel or novel antiandrogen agents (abiraterone, enzalutamide, apalutamide, or darolutamide) or both will be eligible. Genes involved directly or indirectly in the HRR pathway will be tested. In this single-arm phase II study, we will screen approximately 200 patients to enroll 49 patients, and carboplatin (dosing at the area under curve=5) will be administered every 3 weeks until progression or intolerable side effects. The primary end point will be assessed as the proportion of patients with a reduction of serum prostate-specific antigen by more than 50% from enrollment. Secondary outcomes include progression-free survival—soft-tissue disease progression (by response evaluation criteria in solid tumors, version 1.1, and bone lesion progression using Prostate Cancer Clinical Trials Working Group 3 criteria), health-related quality of life during carboplatin treatment using the Functional Assessment of Cancer Therapy—Prostate questionnaire and the European Organisation for Research and Treatment of Cancer questionnaire and safety profile of carboplatin (National Cancer Institute’s Common Terminology Criteria for Adverse Events version 5.0). Results: The trial started enrollment in September 2023. This trial is ongoing, and 12 patients have been recruited to date. All 49 participants will be enrolled according to plan. Conclusions: This prospective phase II trial represents a critical step toward addressing the therapeutic gap in patients with mCRPC harboring HRR pathway mutations, particularly in demographic regions with limited access to poly (ADP-ribose) polymerase inhibitors. Outcomes from this study will inform clinical practice and guide future phase III randomized trials, ultimately improving patient outcomes globally. Trial Registration: Clinical Trials Registry of India CTRI/2023/04/051507; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=Njc0NjU=&Enc=&userName= International Registered Report Identifier (IRRID): DERR1-10.2196/54086 %M 38453159 %R 10.2196/54086 %U https://www.researchprotocols.org/2024/1/e54086 %U https://doi.org/10.2196/54086 %U http://www.ncbi.nlm.nih.gov/pubmed/38453159 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e56572 %T A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection %A Nkoy,Flory L %A Stone,Bryan L %A Zhang,Yue %A Luo,Gang %+ Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican Street, Building C, Box 358047, Seattle, WA, 98195, United States, 1 2062214596, gangluo@cs.wisc.edu %K asthma %K causal inference %K forecasting %K machine learning %K decision support %K drug %K drugs %K pharmacy %K pharmacies %K pharmacology %K pharmacotherapy %K pharmaceutic %K pharmaceutics %K pharmaceuticals %K pharmaceutical %K medication %K medications %K medication selection %K respiratory %K pulmonary %K forecast %K ICS %K inhaled corticosteroid %K inhaler %K inhaled %K corticosteroid %K corticosteroids %K artificial intelligence %K personalized %K customized %D 2024 %7 17.4.2024 %9 Viewpoint %J JMIR Med Inform %G English %X Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources. %M 38630536 %R 10.2196/56572 %U https://medinform.jmir.org/2024/1/e56572 %U https://doi.org/10.2196/56572 %U http://www.ncbi.nlm.nih.gov/pubmed/38630536 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51138 %T A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health %A Washington,Peter %+ Information and Computer Sciences, University of Hawaii at Manoa, 1680 East-West Road, Honolulu, HI, 96822, United States, pyw@hawaii.edu %K crowdsourcing %K digital medicine %K human-in-the-loop %K human in the loop %K human-AI collaboration %K machine learning %K precision health %K artificial intelligence %K AI %D 2024 %7 11.4.2024 %9 Viewpoint %J J Med Internet Res %G English %X Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care. %M 38602750 %R 10.2196/51138 %U https://www.jmir.org/2024/1/e51138 %U https://doi.org/10.2196/51138 %U http://www.ncbi.nlm.nih.gov/pubmed/38602750 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55723 %T Predictive and Prognostic Biomarkers in Patients With Mycosis Fungoides and Sézary Syndrome (BIO-MUSE): Protocol for a Translational Study %A Belfrage,Emma %A Ek,Sara %A Johansson,Åsa %A Brauner,Hanna %A Sonesson,Andreas %A Drott,Kristina %+ Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Skåne University Hospital, Lasarettsgatan 15, Lund, 222 41, Sweden, 46 707367293, emma.belfrage@med.lu.se %K mycosis fungoides %K Sézary syndrome %K prognostic %K predictive %K protocol %K translational study %K cutaneous T-cell lymphomas (CTCL) %K skin microbiota %K immunology %K tissue microenvironment %K epigenetics %K quality of life %K skin infection %K Staphylococcus aureus %K progression of disease %K skin barrier %K prognostic biomarkers %K adult %K adults %K elderly %K spatial %K microbiological sampling %K blood %K study protocol %D 2024 %7 4.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Cutaneous T-cell lymphoma (CTCL) is a rare group of lymphomas that primarily affects the skin. Mycosis fungoides (MF) is the most common form of CTCL and Sézary syndrome (SS) is more infrequent. Early stages (IA-IIA) have a favorable prognosis, while advanced stages (IIB-IVB) have a worse prognosis. Around 25% of patients with early stages of the disease will progress to advanced stages. Malignant skin-infiltrating T-cells in CTCL are accompanied by infiltrates of nonmalignant T-cells and other immune cells that produce cytokines that modulate the inflammation. Skin infection, often with Staphylococcus aureus, is frequent in advanced stages and can lead to sepsis and death. S. aureus has also been reported to contribute to the progression of the disease. Previous reports indicate a shift from Th1 to Th2 cytokine production and dysfunction of the skin barrier in CTCL. Treatment response is highly variable and often unpredictable, and there is a need for new predictive and prognostic biomarkers. Objective: This prospective translational study aims to identify prognostic biomarkers in the blood and skin of patients with MF and SS. Methods: The Predictive and Prognostic Biomarkers in Patients With MF and SS (BIO-MUSE) study aims to recruit 120 adult patients with MF or SS and a control group of 20 healthy volunteers. The treatments will be given according to clinical routine. The sampling of each patient will be performed every 3 months for 3 years. The blood samples will be analyzed for lactate dehydrogenase, immunoglobulin E, interleukins, thymus and activation-regulated chemokine, and lymphocyte subpopulations. The lymphoma microenvironment will be investigated through digital spatial profiling and single-cell RNA sequencing. Microbiological sampling and analysis of skin barrier function will be performed. The life quality parameters will be evaluated. The results will be evaluated by the stage of the disease. Results: Patient inclusion started in 2021 and is still ongoing in 2023, with 18 patients and 20 healthy controls enrolled. The publication of selected translational findings before the publication of the main results of the trial is accepted. Conclusions: This study aims to investigate blood and skin with a focus on immune cells and the microbiological environment to identify potential new prognostic biomarkers in MF and SS. Trial Registration: ClinicalTrials.gov NCT04904146; https://www.clinicaltrials.gov/study/NCT04904146 International Registered Report Identifier (IRRID): DERR1-10.2196/55723 %M 38436589 %R 10.2196/55723 %U https://www.researchprotocols.org/2024/1/e55723 %U https://doi.org/10.2196/55723 %U http://www.ncbi.nlm.nih.gov/pubmed/38436589 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e51326 %T Prediction of Antibiotic Resistance in Patients With a Urinary Tract Infection: Algorithm Development and Validation %A İlhanlı,Nevruz %A Park,Se Yoon %A Kim,Jaewoong %A Ryu,Jee An %A Yardımcı,Ahmet %A Yoon,Dukyong %+ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Yongin, 16995, Republic of Korea, 82 3151898450, dukyong.yoon@yonsei.ac.kr %K antibiotic resistance %K machine learning %K urinary tract infections %K UTI %K decision support %D 2024 %7 29.2.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: The early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection. Objective: In this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed. Methods: The electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients’ admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model’s predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning–based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic. Results: The data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance. Conclusions: The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI. %M 38421718 %R 10.2196/51326 %U https://medinform.jmir.org/2024/1/e51326 %U https://doi.org/10.2196/51326 %U http://www.ncbi.nlm.nih.gov/pubmed/38421718 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e50733 %T Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study %A English,Andrew %A McDaid,Darren %A Lynch,Seodhna M %A McLaughlin,Joseph %A Cooper,Eamonn %A Wingfield,Benjamin %A Kelly,Martin %A Bhavsar,Manav %A McGilligan,Victoria %A Irwin,Rachelle E %A Bucholc,Magda %A Zhang,Shu-Dong %A Shukla,Priyank %A Rai,Taranjit Singh %A Bjourson,Anthony J %A Murray,Elaine %A Gibson,David S %A Walsh,Colum %+ Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Derry/Londonderry, BT47 6SB, United Kingdom, 44 028 7161 1249, d.gibson@ulster.ac.uk %K COVID-19 %K clinical research %K multiomics %K comorbidity %K severity %K electronic health record %D 2024 %7 14.2.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme-2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. Objective: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. Methods: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription-quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12-item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analysis were performed on the plasma. Multiomics data will be combined with clinical data to produce sensitive and specific prognostic models for severity risk. Results: An initial demographic and clinical profile of the NI Cohort A has been completed. A total of 249 hospitalized patients and 270 nonhospitalized patients were recruited, of whom 184 (64.3%) were female, and the mean age was 45.4 (SD 13) years. High levels of comorbidity were evident in the hospitalized cohort, with cardiovascular disease and metabolic and respiratory disorders being the most significant (P<.001), grouped according to the International Classification of Diseases 10 codes. Conclusions: This study will provide a comprehensive opportunity to study the mechanisms of COVID-19 severity in recontactable participants. International Registered Report Identifier (IRRID): DERR1-10.2196/50733 %M 38354037 %R 10.2196/50733 %U https://www.researchprotocols.org/2024/1/e50733 %U https://doi.org/10.2196/50733 %U http://www.ncbi.nlm.nih.gov/pubmed/38354037 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e48527 %T Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis %A Wang,Qingyi %A Chang,Zhuo %A Liu,Xiaofang %A Wang,Yunrui %A Feng,Chuwen %A Ping,Yunlu %A Feng,Xiaoling %+ Department of Gynecology, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, 26 Heping Road, Xiangfang District, Harbin, 150000, China, 86 13604800585, doctorfxl@163.com %K ovarian cancer %K platinum chemotherapy response %K machine learning %K platinum-based therapy %K predictive potential %D 2024 %7 22.1.2024 %9 Review %J J Med Internet Res %G English %X Background: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. Objective: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. Results: A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. Conclusions: Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems. %M 38252469 %R 10.2196/48527 %U https://www.jmir.org/2024/1/e48527 %U https://doi.org/10.2196/48527 %U http://www.ncbi.nlm.nih.gov/pubmed/38252469 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47254 %T The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application %A Blatter,Tobias Ueli %A Witte,Harald %A Fasquelle-Lopez,Jules %A Nakas,Christos Theodoros %A Raisaro,Jean Louis %A Leichtle,Alexander Benedikt %+ University Institute of Clinical Chemistry, University Hospital Bern, Freiburgstrasse 10, Bern, 3010, Switzerland, 41 31 632 83 30, harald.witte@extern.insel.ch %K personalized health %K laboratory medicine %K reference interval %K research infrastructure %K sensitive data %K confidential data %K data security %K differential privacy %K precision medicine %D 2023 %7 18.10.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. Objective: This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group–specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. Methods: A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified “big data” resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict “no copy, no move” principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. Results: The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group–specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. Conclusions: With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine. %M 37851984 %R 10.2196/47254 %U https://www.jmir.org/2023/1/e47254 %U https://doi.org/10.2196/47254 %U http://www.ncbi.nlm.nih.gov/pubmed/37851984 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44895 %T The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis %A Feng,Jing %A Zhang,Qizhi %A Wu,Feng %A Peng,Jinxiang %A Li,Ziwei %A Chen,Zhuang %+ Department of Cardiovascular Medicine, Fifth People’s Hospital of Jinan, 24297 Jingshi Road, Huaiyin District, Jinan, 250000, China, 86 18764026019, humourzhuang@163.com %K machine learning %K ischemic stroke %K onset time %K stroke %D 2023 %7 12.10.2023 %9 Review %J J Med Internet Res %G English %X Background: Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable. Objective: We aimed to assess the value of applying machine learning in predicting the time of stroke onset. Methods: PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team). Results: Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86). Conclusions: Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds. Trial Registration: PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898 %M 37824198 %R 10.2196/44895 %U https://www.jmir.org/2023/1/e44895 %U https://doi.org/10.2196/44895 %U http://www.ncbi.nlm.nih.gov/pubmed/37824198 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44359 %T Use of Biological Feedback as a Health Behavior Change Technique in Adults: Scoping Review %A Richardson,Kelli M %A Jospe,Michelle R %A Saleh,Ahlam A %A Clarke,Thanatcha Nadia %A Bedoya,Arianna R %A Behrens,Nick %A Marano,Kari %A Cigan,Lacey %A Liao,Yue %A Scott,Eric R %A Guo,Jessica S %A Aguinaga,April %A Schembre,Susan M %+ Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, 2115 Wisconsin Ave NW, Washington, DC, 20007, United States, 1 202 444 2223, ss4731@georgetown.edu %K monitoring, physiologic %K biomarkers %K feedback, psychological %K health behavior %K health promotion %K biological %K adults %K biosensing %K technology %K support %K intervention %K electronic database %K cardiovascular disease %K obesity %K device %D 2023 %7 25.9.2023 %9 Review %J J Med Internet Res %G English %X Background: Recent advancements in personal biosensing technology support the shift from standardized to personalized health interventions, whereby biological data are used to motivate health behavior change. However, the implementation of interventions using biological feedback as a behavior change technique has not been comprehensively explored. Objective: The purpose of this review was to (1) map the domains of research where biological feedback has been used as a behavior change technique and (2) describe how it is implemented in behavior change interventions for adults. Methods: A comprehensive systematic search strategy was used to query 5 electronic databases (Ovid MEDLINE, Elsevier Embase, Cochrane Central Register of Controlled Trials, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global) in June 2021. Eligible studies were primary analyses of randomized controlled trials (RCTs) in adults that incorporated biological feedback as a behavior change technique. DistillerSR was used to manage the literature search and review. Results: After removing 49,500 duplicates, 50,287 articles were screened and 767 articles were included. The earliest RCT was published in 1972 with a notable increase in publications after 2000. Biological feedback was most used in RCTs aimed at preventing or managing diabetes (n=233, 30.4%), cardiovascular disease (n=175, 22.8%), and obesity (n=115, 15%). Feedback was often given on multiple biomarkers and targeted multiple health behaviors. The most common biomarkers used were anthropometric measures (n=297, 38.7%), blood pressure (n=238, 31%), and glucose (n=227, 29.6%). The most targeted behaviors were diet (n=472, 61.5%), physical activity (n=417, 54.4%), and smoking reduction (n=154, 20.1%). The frequency and type of communication by which biological feedback was provided varied by the method of biomarker measurement. Of the 493 (64.3%) studies where participants self-measured their biomarker, 476 (96.6%) received feedback multiple times over the intervention and 468 (94.9%) received feedback through a biosensing device. Conclusions: Biological feedback is increasingly being used to motivate behavior change, particularly where relevant biomarkers can be readily assessed. Yet, the methods by which biological feedback is operationalized in intervention research varied, and its effectiveness remains unclear. This scoping review serves as the foundation for developing a guiding framework for effectively implementing biological feedback as a behavior change technique. Trial Registration: Open Science Framework Registries; https://doi.org/10.17605/OSF.IO/YP5WAd International Registered Report Identifier (IRRID): RR2-10.2196/32579 %M 37747766 %R 10.2196/44359 %U https://www.jmir.org/2023/1/e44359 %U https://doi.org/10.2196/44359 %U http://www.ncbi.nlm.nih.gov/pubmed/37747766 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e48247 %T Multidimensional Analysis of a Cell-Free DNA Whole Methylome Sequencing Assay for Early Detection of Gastric Cancer: Protocol for an Observational Case-Control Study %A Han,Yongjun %A Wei,Jiangpeng %A Wang,Weidong %A Gao,Ruiqi %A Shen,Ning %A Song,Xiaofeng %A Ni,Yang %A Li,Yulong %A Xu,Li-Di %A Chen,Weizhi %A Li,Xiaohua %+ Department of Gastrointestinal Surgery, Xijing Hospital, Air Force Military Medical University, 127 Changle West Road, Xincheng District, Xi'an, 710033, China, 86 19991901686, xjyylixiaohua@163.com %K gastric cancer %K circulating cell-free DNA %K early detection %K methylation %K fragmentation %K chromosomal instability %K whole methylome sequencing %K multidimensional model %D 2023 %7 20.9.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Commonly used noninvasive serological indicators serve as a step before endoscope diagnosis and help identify the high-risk gastric cancer (GC) population. However, they are associated with high false positives and high false negatives. Alternative noninvasive approaches, such as cancer-related features in cell-free DNA (cfDNA) fragments, have been gradually identified and play essential roles in early cancer detection. The integrated analysis of multiple cfDNA features has enhanced detection sensitivity compared to individual features. Objective: This study aimed to develop and validate an assay based on assessing genomic-scale methylation and fragmentation profiles of plasma cfDNA for early cancer detection, thereby facilitating the early diagnosis of GC. The primary objective is to evaluate the overall specificity and sensitivity of the assay in predicting GC within the entire cohort, and subsequently within each clinical stage of GC. The secondary objective involved investigating the specificity and sensitivity of the assay in combination with possible serological indicators. Methods: This is an observational case-control study. Blood samples will be prospectively collected before gastroscopy from 180 patients with GC and 180 nonmalignant control subjects (healthy or with benign gastric diseases). Cases and controls will be randomly divided into a training and a testing data set at a ratio of 2:1. Plasma cfDNA will be isolated and extracted, followed by bisulfite-free low-depth whole methylome sequencing. A multidimensional model named Thorough Epigenetic Marker Integration Solution (THEMIS) will be constructed in the training data set. The model includes features such as the methylated fragment ratio, chromosomal aneuploidy of featured fragments, fragment size index, and fragment end motif. The performance of the model in distinguishing between patients with cancer and noncancer controls will then be evaluated in the testing data set. Furthermore, GC-related biomarkers, such as pepsinogen, gastrin-17, and Helicobacter pylori, will be measured for each patient, and their predictive accuracy will be assessed both independently and in combination with the THEMIS model Results: Recruitment began in November 2022 and will be ended in April 2024. As of August 2022,250 patients have been enrolled. The final data analysis is anticipated to be completed by September 2024. Conclusions: This is the first registered case-control study designed to investigate a stacked ensemble model integrating several cfDNA features generated from a bisulfite-free whole methylome sequencing assay. These features include methylation patterns, fragmentation profiles, and chromosomal copy number changes, with the aim of identifying the GC population. This study will determine whether multidimensional analysis of cfDNA will prove to be an effective strategy for distinguishing patients with GC from nonmalignant individuals within the Chinese population. We anticipate the THEMIS model will complement the standard-of-care screening and aid in identifying high-risk patients for further diagnosis. Trial Registration: ClinicalTrial.gov NCT05668910; https://www.clinicaltrials.gov/study/NCT05668910 International Registered Report Identifier (IRRID): DERR1-10.2196/48247 %M 37728978 %R 10.2196/48247 %U https://www.researchprotocols.org/2023/1/e48247 %U https://doi.org/10.2196/48247 %U http://www.ncbi.nlm.nih.gov/pubmed/37728978 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 9 %N %P e46752 %T Training Physicians in the Digital Health Era: How to Leverage the Residency Elective %A Hsiang,Esther Y %A Ganeshan,Smitha %A Patel,Saharsh %A Yurkovic,Alexandra %A Parekh,Ami %+ Department of Medicine, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA, 94143, United States, 1 415 476 1000, estherhsiang@gmail.com %K digital health %K care delivery innovation %K physician-leader %K medical training %K residency education %K eHealth %K residency %K medical education %K software %K elective %K intern %K telehealth %K telemedicine %D 2023 %7 14.7.2023 %9 Viewpoint %J JMIR Med Educ %G English %X Digital health is an expanding field and is fundamentally changing the ways health care can be delivered to patients. Despite the changing landscape of health care delivery, medical trainees are not routinely exposed to digital health during training. In this viewpoint, we argue that thoughtfully implemented immersive elective internships with digital health organizations, including start-ups, during residency are valuable for residents, residency programs, and digital health companies. This viewpoint represents the opinions of the authors based on their experience as resident physicians working as interns within a start-up health navigation and telehealth company. First, residents were able to apply their expertise beyond the traditional clinical environment, use creativity to solve health care problems, and learn from different disciplines not typically encountered by most physicians in traditional clinical practice. Second, residency programs were able to strengthen their program’s educational offerings and better meet the needs of a heterogenous group of residents who are increasingly seeking nontraditional ways to learn more about care delivery transformation. Third, digital health companies were able to expand their clinical team and receive new insights from physicians in training. We believe that immersive elective internships for physicians in training provide opportunities for experiential learning in a fast-paced environment within a field that is rapidly evolving. By creating similar experiences for other resident physicians, residency programs and digital health companies have a key opportunity to influence future physician-leaders and health care innovators. %M 37450323 %R 10.2196/46752 %U https://mededu.jmir.org/2023/1/e46752 %U https://doi.org/10.2196/46752 %U http://www.ncbi.nlm.nih.gov/pubmed/37450323 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45614 %T Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study %A Boussina,Aaron %A Wardi,Gabriel %A Shashikumar,Supreeth Prajwal %A Malhotra,Atul %A Zheng,Kai %A Nemati,Shamim %+ Division of Biomedical Informatics, University of California, San Diego, 9500 Gilman Dr. MC 0990, La Jolla, CA, 92093, United States, 1 858 534 2230, aboussina@health.ucsd.edu %K sepsis %K phenotype %K emergency service, hospital %K disease progression %K artificial intelligence %K machine learning %K emergency %K infection %K clinical phenotype %K clinical phenotyping %K transition model %K transition modeling %D 2023 %7 23.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient’s physiological state and the interventions they receive. Objective: We aimed to develop a novel approach for deriving clinical phenotypes using unsupervised learning and transition modeling. Methods: Forty commonly used clinical variables from the electronic health record were used as inputs to a feed-forward neural network trained to predict the onset of sepsis. Using spectral clustering on the representations from this network, we derived and validated consistent phenotypes across a diverse cohort of patients with sepsis. We modeled phenotype dynamics as a Markov decision process with transitions as a function of the patient’s current state and the interventions they received. Results: Four consistent and distinct phenotypes were derived from over 11,500 adult patients who were admitted from the University of California, San Diego emergency department (ED) with sepsis between January 1, 2016, and January 31, 2020. Over 2000 adult patients admitted from the University of California, Irvine ED with sepsis between November 4, 2017, and August 4, 2022, were involved in the external validation. We demonstrate that sepsis phenotypes are not static and evolve in response to physiological factors and based on interventions. We show that roughly 45% of patients change phenotype membership within the first 6 hours of ED arrival. We observed consistent trends in patient dynamics as a function of interventions including early administration of antibiotics. Conclusions: We derived and describe 4 sepsis phenotypes present within 6 hours of triage in the ED. We observe that the administration of a 30 mL/kg fluid bolus may be associated with worse outcomes in certain phenotypes, whereas prompt antimicrobial therapy is associated with improved outcomes. %M 37351927 %R 10.2196/45614 %U https://www.jmir.org/2023/1/e45614 %U https://doi.org/10.2196/45614 %U http://www.ncbi.nlm.nih.gov/pubmed/37351927 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42822 %T A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study %A Sinaci,A Anil %A Gencturk,Mert %A Teoman,Huseyin Alper %A Laleci Erturkmen,Gokce Banu %A Alvarez-Romero,Celia %A Martinez-Garcia,Alicia %A Poblador-Plou,Beatriz %A Carmona-Pírez,Jonás %A Löbe,Matthias %A Parra-Calderon,Carlos Luis %+ Software Research & Development and Consultancy Corporation (SRDC), Orta Dogu Teknik Universitesi Teknokent K1-16, Cankaya, 06800, Turkey, 90 3122101763, anil@srdc.com.tr %K Health Level 7 Fast Healthcare Interoperability Resources %K HL7 FHIR %K Findable, Accessible, Interoperable, and Reusable principles %K FAIR principles %K health data sharing %K health data transformation %K secondary use %D 2023 %7 8.3.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. Objective: Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. Methods: Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. Results: Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. Conclusions: We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks. %M 36884270 %R 10.2196/42822 %U https://www.jmir.org/2023/1/e42822 %U https://doi.org/10.2196/42822 %U http://www.ncbi.nlm.nih.gov/pubmed/36884270 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e29279 %T SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study %A Suraj,Varun %A Del Vecchio Fitz,Catherine %A Kleiman,Laura B %A Bhavnani,Suresh K %A Jani,Chinmay %A Shah,Surbhi %A McKay,Rana R %A Warner,Jeremy %A Alterovitz,Gil %+ Medicine and Biomedical Informatics, Vanderbilt University, 2525 West End Ave, Suite 1500, Nashville, TN, 37203, United States, 1 615 936 3524, jeremy.warner@vumc.org %K COVID-19 %K clinical decision support %K precision medicine %K web application %K FHIR %D 2022 %7 18.2.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19’s effect on patients with certain pre-existing conditions. Objective: In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient’s electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19. Methods: The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient’s medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient’s medical history will affect their COVID-19 outcomes. Results: The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources–enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined. Conclusions: The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient’s medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions. %M 34932493 %R 10.2196/29279 %U https://www.jmir.org/2022/2/e29279 %U https://doi.org/10.2196/29279 %U http://www.ncbi.nlm.nih.gov/pubmed/34932493 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e34560 %T Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience %A Bove,Riley %A Schleimer,Erica %A Sukhanov,Paul %A Gilson,Michael %A Law,Sindy M %A Barnecut,Andrew %A Miller,Bruce L %A Hauser,Stephen L %A Sanders,Stephan J %A Rankin,Katherine P %+ UCSF Weill Institute for Neurosciences, University of California, San Francisco, 1651 4th Street, San Francisco, CA, 94158, United States, 1 415 353 2069, Riley.bove@ucsf.edu %K precision medicine %K clinical implementation %K in silico trials %K clinical dashboard %K precision %K implementation %K dashboard %K design %K experience %K analytic %K tool %K analysis %K decision-making %K real time %K platform %K human-centered design %D 2022 %7 15.2.2022 %9 Viewpoint %J J Med Internet Res %G English %X Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users’ specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient’s electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine. %M 35166689 %R 10.2196/34560 %U https://www.jmir.org/2022/2/e34560 %U https://doi.org/10.2196/34560 %U http://www.ncbi.nlm.nih.gov/pubmed/35166689 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e30291 %T Long-Term Effects of a Web-Based Low-FODMAP Diet Versus Probiotic Treatment for Irritable Bowel Syndrome, Including Shotgun Analyses of Microbiota: Randomized, Double-Crossover Clinical Trial %A Ankersen,Dorit Vedel %A Weimers,Petra %A Bennedsen,Mette %A Haaber,Anne Birgitte %A Fjordside,Eva Lund %A Beber,Moritz Emanuel %A Lieven,Christian %A Saboori,Sanaz %A Vad,Nicolai %A Rannem,Terje %A Marker,Dorte %A Paridaens,Kristine %A Frahm,Suzanne %A Jensen,Lisbeth %A Rosager Hansen,Malte %A Burisch,Johan %A Munkholm,Pia %+ Department of Gastroenterology, North Zealand University Hospital, Frederikssundsvej 30, Frederikssund, 3600, Denmark, 45 48292078, pia.munkholm@regionh.dk %K irritable bowel syndrome %K web-based low-FODMAP diet %K probiotics %K randomized trial %K web-based %K IBS %K symptom management %K treatment outcomes %K outcomes %K treatment %K microbiota %K microbiome %K gastroenterology %K mobile app %K mHealth %K eHealth %D 2021 %7 14.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The long-term management of irritable bowel syndrome (IBS) poses many challenges. In short-term studies, eHealth interventions have been demonstrated to be safe and practical for at-home monitoring of the effects of probiotic treatments and a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs). IBS has been linked to alterations in the microbiota. Objective: The aim of this study was to determine whether a web-based low-FODMAP diet (LFD) intervention and probiotic treatment were equally good at reducing IBS symptoms, and whether the response to treatments could be explained by patients’ microbiota. Methods: Adult IBS patients were enrolled in an open-label, randomized crossover trial (for nonresponders) with 1 year of follow-up using the web application IBS Constant Care (IBS CC). Patients were recruited from the outpatient clinic at the Department of Gastroenterology, North Zealand University Hospital, Denmark. Patients received either VSL#3 for 4 weeks (2 × 450 billion colony-forming units per day) or were placed on an LFD for 4 weeks. Patients responding to the LFD were reintroduced to foods high in FODMAPs, and probiotic responders received treatments whenever they experienced a flare-up of symptoms. Treatment response and symptom flare-ups were defined as a reduction or increase, respectively, of at least 50 points on the IBS Severity Scoring System (IBS-SSS). Web-based ward rounds were performed daily by the study investigator. Fecal microbiota were analyzed by shotgun metagenomic sequencing (at least 10 million 2 × 100 bp paired-end sequencing reads per sample). Results: A total of 34 IBS patients without comorbidities and 6 healthy controls were enrolled in the study. Taken from participating subjects, 180 fecal samples were analyzed for their microbiota composition. Out of 21 IBS patients, 12 (57%) responded to the LFD and 8 (38%) completed the reintroduction of FODMAPs. Out of 21 patients, 13 (62%) responded to their first treatment of VSL#3 and 7 (33%) responded to multiple VSL#3 treatments. A median of 3 (IQR 2.25-3.75) probiotic treatments were needed for sustained symptom control. LFD responders were reintroduced to a median of 14.50 (IQR 7.25-21.75) high-FODMAP items. No significant difference in the median reduction of IBS-SSS for LFD versus probiotic responders was observed, where for LFD it was –126.50 (IQR –196.75 to –76.75) and for VSL#3 it was –130.00 (IQR –211.00 to –70.50; P>.99). Responses to either of the two treatments were not able to be predicted using patients’ microbiota. Conclusions: The web-based LFD intervention and probiotic treatment were equally efficacious in managing IBS symptoms. The response to treatments could not be explained by the composition of the microbiota. The IBS CC web application was shown to be practical, safe, and useful for clinical decision making in the long-term management of IBS. Although this study was underpowered, findings from this study warrant further research in a larger sample of patients with IBS to confirm these long-term outcomes. Trial Registration: ClinicalTrials.gov NCT03586622; https://clinicaltrials.gov/ct2/show/NCT03586622 %M 34904950 %R 10.2196/30291 %U https://www.jmir.org/2021/12/e30291 %U https://doi.org/10.2196/30291 %U http://www.ncbi.nlm.nih.gov/pubmed/34904950 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e31121 %T An Integrated, Scalable, Electronic Video Consent Process to Power Precision Health Research: Large, Population-Based, Cohort Implementation and Scalability Study %A Lajonchere,Clara %A Naeim,Arash %A Dry,Sarah %A Wenger,Neil %A Elashoff,David %A Vangala,Sitaram %A Petruse,Antonia %A Ariannejad,Maryam %A Magyar,Clara %A Johansen,Liliana %A Werre,Gabriela %A Kroloff,Maxwell %A Geschwind,Daniel %+ Center for SMART Health, Institute for Precision Health, David Geffen School of Medicine at UCLA, 10911 Weyburn Ave, Suite 300e, Los Angeles, CA, 90095, United States, 1 3103670148, anaeim@mednet.ucla.edu %K biobanking %K precision medicine %K electronic consent %K privacy %K consent %K patient privacy %K clinical data %K eHealth %K recruitment %K population health %K data collection %K research methods %K video %K research %K validation %K scalability %D 2021 %7 8.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Obtaining explicit consent from patients to use their remnant biological samples and deidentified clinical data for research is essential for advancing precision medicine. Objective: We aimed to describe the operational implementation and scalability of an electronic universal consent process that was used to power an institutional precision health biobank across a large academic health system. Methods: The University of California, Los Angeles, implemented the use of innovative electronic consent videos as the primary recruitment tool for precision health research. The consent videos targeted patients aged ≥18 years across ambulatory clinical laboratories, perioperative settings, and hospital settings. Each of these major areas had slightly different workflows and patient populations. Sociodemographic information, comorbidity data, health utilization data (ambulatory visits, emergency room visits, and hospital admissions), and consent decision data were collected. Results: The consenting approach proved scalable across 22 clinical sites (hospital and ambulatory settings). Over 40,000 participants completed the consent process at a rate of 800 to 1000 patients per week over a 2-year time period. Participants were representative of the adult University of California, Los Angeles, Health population. The opt-in rates in the perioperative (16,500/22,519, 73.3%) and ambulatory clinics (2308/3390, 68.1%) were higher than those in clinical laboratories (7506/14,235, 52.7%; P<.001). Patients with higher medical acuity were more likely to opt in. The multivariate analyses showed that African American (odds ratio [OR] 0.53, 95% CI 0.49-0.58; P<.001), Asian (OR 0.72, 95% CI 0.68-0.77; P<.001), and multiple-race populations (OR 0.73, 95% CI 0.69-0.77; P<.001) were less likely to participate than White individuals. Conclusions: This is one of the few large-scale, electronic video–based consent implementation programs that reports a 65.5% (26,314/40,144) average overall opt-in rate across a large academic health system. This rate is higher than those previously reported for email (3.6%) and electronic biobank (50%) informed consent rates. This study demonstrates a scalable recruitment approach for population health research. %M 34889741 %R 10.2196/31121 %U https://www.jmir.org/2021/12/e31121 %U https://doi.org/10.2196/31121 %U http://www.ncbi.nlm.nih.gov/pubmed/34889741 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 11 %P e33047 %T Optimizing Coaching During Web-Based Relationship Education for Low-Income Couples: Protocol for Precision Medicine Research %A Hatch,S Gabe %A Lobaina,Diana %A Doss,Brian D %+ Department of Psychology, University of Miami, 5665 Ponce De Leon Blvd, Office 446, Coral Gables, FL, 33146, United States, 1 305 288 1587, sgh49@miami.edu %K online relationship education %K precision medicine %K low-income couples %K coaching %K OurRelationship %K ePREP %D 2021 %7 4.11.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: In-person relationship education classes funded by the federal government tend to experience relatively high attrition rates and have only a limited effect on relationships. In contrast, low-income couples tend to report meaningful gains from web-based relationship education when provided with individualized coach contact. However, little is known about the method and intensity of practitioner contact that a couple requires to complete the web-based program and receive the intended benefit. Objective: The aim of this study is to use within-group models to create an algorithm to assign future couples to different programs and levels of coach contact, identify the most powerful predictors of treatment adherence and gains in relationship satisfaction within 3 different levels of coaching, and examine the most powerful predictors of treatment adherence and gains in relationship satisfaction among the 3 levels of coach contact. Methods: To accomplish these goals, this project intends to use data from a web-based Sequential Multiple Assignment Randomized Trial of the OurRelationship and web-based Prevention and Relationship Enhancement programs, in which the method and type of coach contact were randomly varied across 1248 couples (2496 individuals), with the hope of advancing theory in this area and generating accurate predictions. This study was funded by the US Department of Health and Human Services, Administration for Children and Families (grant number 90PD0309). Results: Data collection from the Sequential Multiple Assignment Randomized Trial of the OurRelationship and web-based Prevention and Relationship Enhancement Program was completed in October of 2020. Conclusions: Some of the direct benefits of this study include benefits to social services program administrators, tailoring of more effective relationship education, and effective delivery of evidence- and web-based relationship health interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/33047 %M 34734838 %R 10.2196/33047 %U https://www.researchprotocols.org/2021/11/e33047 %U https://doi.org/10.2196/33047 %U http://www.ncbi.nlm.nih.gov/pubmed/34734838 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 9 %P e26220 %T Virtual Clinical and Precision Medicine Tumor Boards—Cloud-Based Platform–Mediated Implementation of Multidisciplinary Reviews Among Oncology Centers in the COVID-19 Era: Protocol for an Observational Study %A Blasi,Livio %A Bordonaro,Roberto %A Serretta,Vincenzo %A Piazza,Dario %A Firenze,Alberto %A Gebbia,Vittorio %+ La Maddalena Cancer Center, via San Lorenzo Colli n 312d, 90100, Palermo, 90100, Italy, 39 +39 091 6806710, vittorio.gebbia@gmail.com %K virtual tumor board %K multidisciplinary collaboration %K oncology %K multidisciplinary communication %K health services %K multidisciplinary oncology consultations %K virtual health %K digital health %K precision medicine %K tumor %K cancer %K cloud-based %K platform %K implementation %K oncology %K COVID-19 %D 2021 %7 10.9.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Multidisciplinary tumor boards play a pivotal role in the patient-centered clinical management and in the decision-making process to provide best evidence-based, diagnostic, and therapeutic care to patients with cancer. Among the barriers to achieve an efficient multidisciplinary tumor board, lack of time and geographical distance play a major role. Therefore, the elaboration of an efficient virtual multidisciplinary tumor board (VMTB) is a key point to successfully obtain an oncology team and implement a network among health professionals and institutions. This need is stronger than ever during the COVID-19 pandemic. Objective: This paper presents a research protocol for an observational study focused on exploring the structuring process and the implementation of a multi-institutional VMTB in Sicily, Italy. Other endpoints include analysis of cooperation between participants, adherence to guidelines, patients’ outcomes, and patient satisfaction. Methods: This protocol encompasses a pragmatic, observational, multicenter, noninterventional, prospective trial. The study’s programmed duration is 5 years, with a half-yearly analysis of the primary and secondary objectives’ measurements. Oncology care health professionals from various oncology subspecialties at oncology departments in multiple hospitals (academic and general hospitals as well as tertiary centers and community hospitals) are involved in a nonhierarchic manner. VMTB employs an innovative, virtual, cloud-based platform to share anonymized medical data that are discussed via a videoconferencing system both satisfying security criteria and compliance with the Health Insurance Portability and Accountability Act. Results: The protocol is part of a larger research project on communication and multidisciplinary collaboration in oncology units and departments spread in the Sicily region. The results of this study will particularly focus on the organization of VMTBs, involving oncology units present in different hospitals spread in the area, and creating a network to allow best patient care pathways and a hub-and-spoke relationship. The present results will also include data concerning organization skills and pitfalls, barriers, efficiency, number, and types with respect to clinical cases and customer satisfaction. Conclusions: VMTB represents a unique opportunity to optimize patient management through a patient-centered approach. An efficient virtualization and data-banking system is potentially time-saving, a source for outcome data, and a detector of possible holes in the hull of clinical pathways. The observations and results from this VMTB study may hopefully be useful to design nonclinical and organizational interventions that enhance multidisciplinary decision-making in oncology. International Registered Report Identifier (IRRID): DERR1-10.2196/26220 %M 34387553 %R 10.2196/26220 %U https://www.researchprotocols.org/2021/9/e26220 %U https://doi.org/10.2196/26220 %U http://www.ncbi.nlm.nih.gov/pubmed/34387553 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e29123 %T Electronic Video Consent to Power Precision Health Research: A Pilot Cohort Study %A Naeim,Arash %A Dry,Sarah %A Elashoff,David %A Xie,Zhuoer %A Petruse,Antonia %A Magyar,Clara %A Johansen,Liliana %A Werre,Gabriela %A Lajonchere,Clara %A Wenger,Neil %+ UCLA Center for SMART Health, Clinical and Translational Science Institute, David Geffen School of Medicine at UCLA, 10911 Weyburn Ave, Los Angeles, CA, 90095, United States, 1 3107948118, anaeim@mednet.ucla.edu %K biobanking %K precision medicine %K electronic consent %K privacy %K pilot study %K video %K consent %K precision %K innovation %K efficient %K precision medicine %K cancer %K education %K barrier %K engagement %K participation %D 2021 %7 8.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Developing innovative, efficient, and institutionally scalable biospecimen consent for remnant tissue that meets the National Institutes of Health consent guidelines for genomic and molecular analysis is essential for precision medicine efforts in cancer. Objective: This study aims to pilot-test an electronic video consent that individuals could complete largely on their own. Methods: The University of California, Los Angeles developed a video consenting approach designed to be comprehensive yet fast (around 5 minutes) for providing universal consent for remnant biospecimen collection for research. The approach was piloted in 175 patients who were coming in for routine services in laboratory medicine, radiology, oncology, and hospital admissions. The pilot yielded 164 completed postconsent surveys. The pilot assessed the usefulness, ease, and trustworthiness of the video consent. In addition, we explored drivers for opting in or opting out. Results: The pilot demonstrated that the electronic video consent was well received by patients, with high scores for usefulness, ease, and trustworthiness even among patients that opted out of participation. The revised more animated video pilot test in phase 2 was better received in terms of ease of use (P=.005) and the ability to understand the information (P<.001). There were significant differences between those who opted in and opted out in their beliefs concerning the usefulness of tissue, trusting researchers, the importance of contributing to science, and privacy risk (P<.001). The results showed that “I trust researchers to use leftover biological specimens to promote the public’s health” and “Sharing a biological sample for research is safe because of the privacy protections in place” discriminated opt-in statuses were the strongest predictors (both areas under the curve were 0.88). Privacy concerns seemed universal in individuals who opted out. Conclusions: Efforts to better educate the community may be needed to help overcome some of the barriers in engaging individuals to participate in precision health initiatives. %M 34313247 %R 10.2196/29123 %U https://formative.jmir.org/2021/9/e29123 %U https://doi.org/10.2196/29123 %U http://www.ncbi.nlm.nih.gov/pubmed/34313247 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e25789 %T Characterization of Global Research Trends and Prospects on Single-Cell Sequencing Technology: Bibliometric Analysis %A Wang,Quan %A Yang,Ke-Lu %A Zhang,Zhen %A Wang,Zhu %A Li,Chen %A Li,Lun %A Tian,Jin-Hui %A Ye,Ying-Jiang %A Wang,Shan %A Jiang,Ke-Wei %+ Department of Gastroenterological Surgery, Peking University People’s Hospital, No.11 Xizhimen South Street, Xicheng District, Beijing, 100044, China, 86 010 88326600, jiangkewei@pkuph.edu.cn %K single-cell sequencing %K bibliometric analysis %K cancer %K cancer genomics %K bioinformatics %K cancer subtyping %K tumor dissociation %K tumor microenvironment %K precision medicine %K immunology %K development trends %K hotspots %K research topics %K Web of Science %K CiteSpace %K VOSviewer %K network %D 2021 %7 10.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: As single-cell sequencing technology has been gradually introduced, it is essential to characterize global collaboration networks and map development trends over the past 20 years. Objective: The aim of this paper was to illustrate collaboration in the field of single-cell sequencing methods and explore key topics and future directions. Methods: Bibliometric analyses were conducted with CiteSpace and VOSviewer software on publications prior to November 2019 from the Web of Science Core Collection about single-cell sequencing methods. Results: Ultimately, we identified 2489 records, which were published in 495 journals by 14,202 authors from 1970 institutes in 61 countries. There was a noticeable increase in publications in 2014. The United States and high-income countries in Europe contributed to most of the records included. Harvard University, Stanford University, Karolinska Institutes, Peking University, and the University of Washington were the biggest nodes in every cluster of the collaboration network, and SA Teichmann, JC Marioni, A Regev, and FC Tang were the top-producing authors. Keywords co-occurrence analysis suggested applications in immunology as a developing research trend. Conclusions: We concluded that the global collaboration network was unformed and that high-income countries contributed more to the rapidly growth of publications of single-cell sequencing technology. Furthermore, the application in immunology might be the next research hotspot and developmental direction. %M 34014832 %R 10.2196/25789 %U https://www.jmir.org/2021/8/e25789 %U https://doi.org/10.2196/25789 %U http://www.ncbi.nlm.nih.gov/pubmed/34014832 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 6 %P e28272 %T Document Retrieval for Precision Medicine Using a Deep Learning Ensemble Method %A Liu,Zhiqiang %A Feng,Jingkun %A Yang,Zhihao %A Wang,Lei %+ College of Computer Science and Technology, Dalian University of Technology, No. 2 Ling Gong Road, Gan Jing Zi District, Dalian, China, 86 131 9011 4398, yangzh@dlut.edu.cn %K biomedical information retrieval %K document ranking %K precision medicine %K deep learning %D 2021 %7 29.6.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: With the development of biomedicine, the number of biomedical documents has increased rapidly bringing a great challenge for researchers trying to retrieve the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in specific retrieval tasks, thereby increasing the difficulty of biomedical information retrieval. Objective: This study aimed to find a more systematic method for retrieving relevant scientific literature for a given patient. Methods: In the initial retrieval stage, we supplemented query terms through query expansion strategies and applied query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employed a text classification model and relevance matching model to evaluate documents from different dimensions and then combined the outputs through logistic regression to re-rank all the documents from the initial ranking list. Results: The proposed ensemble method contributed to the improvement of biomedical retrieval performance. Compared with the existing deep learning–based methods, experimental results showed that our method achieved state-of-the-art performance on the data collection provided by the Text Retrieval Conference 2019 Precision Medicine Track. Conclusions: In this paper, we proposed a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and relevance matching model better captured semantic context information and improved retrieval performance. %M 34185006 %R 10.2196/28272 %U https://medinform.jmir.org/2021/6/e28272 %U https://doi.org/10.2196/28272 %U http://www.ncbi.nlm.nih.gov/pubmed/34185006 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e17137 %T Team Science in Precision Medicine: Study of Coleadership and Coauthorship Across Health Organizations %A An,Ning %A Mattison,John %A Chen,Xinyu %A Alterovitz,Gil %+ Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, United States, 1 617 329 1445, gil_alterovitz@hms.harvard.edu %K precision medicine %K team science %D 2021 %7 14.6.2021 %9 Viewpoint %J J Med Internet Res %G English %X Background: Interdisciplinary collaborations bring lots of benefits to researchers in multiple areas, including precision medicine. Objective: This viewpoint aims at studying how cross-institution team science would affect the development of precision medicine. Methods: Publications of organizations on the eHealth Catalogue of Activities were collected in 2015 and 2017. The significance of the correlation between coleadership and coauthorship among different organizations was calculated using the Pearson chi-square test of independence. Other nonparametric tests examined whether organizations with coleaders publish more and better papers than organizations without coleaders. Results: A total of 374 publications from 69 organizations were analyzed in 2015, and 7064 papers from 87 organizations were analyzed in 2017. Organizations with coleadership published more papers (P<.001, 2015 and 2017), which received higher citations (Z=–13.547, P<.001, 2017), compared to those without coleadership. Organizations with coleaders tended to publish papers together (P<.001, 2015 and 2017). Conclusions: Our findings suggest that organizations in the field of precision medicine could greatly benefit from institutional-level team science. As a result, stronger collaboration is recommended. %M 34125070 %R 10.2196/17137 %U https://www.jmir.org/2021/6/e17137 %U https://doi.org/10.2196/17137 %U http://www.ncbi.nlm.nih.gov/pubmed/34125070 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 5 %P e23586 %T Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study %A Zong,Nansu %A Ngo,Victoria %A Stone,Daniel J %A Wen,Andrew %A Zhao,Yiqing %A Yu,Yue %A Liu,Sijia %A Huang,Ming %A Wang,Chen %A Jiang,Guoqian %+ Department of Health Sciences Research, Mayo Clinic, 200 First Street, Rochester, MN , United States, 1 480 301 8000, Jiang.Guoqian@mayo.edu %K genetic reports %K electronic health records %K predicting primary cancers %K Fast Healthcare Interoperability Resources %K FHIR %K Resource Description Framework %K RDF %D 2021 %7 25.5.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. Objective: This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries. Methods: We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance. Results: With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review. Conclusions: Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer. %M 34032581 %R 10.2196/23586 %U https://medinform.jmir.org/2021/5/e23586 %U https://doi.org/10.2196/23586 %U http://www.ncbi.nlm.nih.gov/pubmed/34032581 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e25401 %T Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study %A Sinha,Ranjan %A Kachru,Dashyanng %A Ricchetti,Roshni Ray %A Singh-Rambiritch,Simitha %A Muthukumar,Karthik Marimuthu %A Singaravel,Vidhya %A Irudayanathan,Carmel %A Reddy-Sinha,Chandana %A Junaid,Imran %A Sharma,Garima %A Francis-Lyon,Patricia Alice %+ Digbi Health, 13105 Delson Ct, Los Altos, CA, 94022, United States, 1 510 883 3721, patricia@digbihealth.com %K obesity %K digital therapeutics %K precision nutrition %K nutrigenomics %K personalized nutrition %K mHealth %K mobile apps %K gut microbiota %K machine learning %K health coaching %K lifestyle medicine %K mobile phone %D 2021 %7 19.5.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions. Objective: This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone. Methods: A cohort of 393 participants enrolled in Digbi Health’s personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data. Results: Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks. Conclusions: Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants’ genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficacy. %M 33849843 %R 10.2196/25401 %U https://www.jmir.org/2021/5/e25401 %U https://doi.org/10.2196/25401 %U http://www.ncbi.nlm.nih.gov/pubmed/33849843 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e26261 %T Fast Healthcare Interoperability Resources (FHIR)–Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study %A Seong,Donghyeong %A Jung,Sungwon %A Bae,Sungchul %A Chung,Jongsuk %A Son,Dae-Soon %A Yi,Byoung-Kee %+ Smart Healthcare Research Institute, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea, 82 2 3410 1944, byoungkeeyi@gmail.com %K FHIR %K clinical NGS genomic testing %K clinical massive parallel sequencing %K quality control %K genomic reporting %D 2021 %7 28.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process. Objective: The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)–based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests. Methods: We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server. Results: Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution. Conclusions: This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data. %M 33908889 %R 10.2196/26261 %U https://www.jmir.org/2021/4/e26261 %U https://doi.org/10.2196/26261 %U http://www.ncbi.nlm.nih.gov/pubmed/33908889 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e21023 %T Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr): Cohort Study %A Li,Jianqiao %A Hojlo,Margaret A %A Chennuri,Sampath %A Gujral,Nitin %A Paterson,Heather L %A Shefchek,Kent A %A Genetti,Casie A %A Cohn,Emily L %A Sewalk,Kara C %A Garvey,Emily A %A Buttermore,Elizabeth D %A Anderson,Nickesha C %A Beggs,Alan H %A Agrawal,Pankaj B %A Brownstein,John S %A Haendel,Melissa A %A Holm,Ingrid A %A Gonzalez-Heydrich,Joseph %A Brownstein,Catherine A %+ Division of Genetics and Genomics, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, 02115, United States, 1 6173554764, catherine.brownstein@childrens.harvard.edu %K self-phenotyping %K 16p13.11 microduplication syndrome %K copy number variation %K genetics %K incomplete penetrance %K phenotype %K variable presentation %K human phenotype ontology %K online survey %K digital health %D 2021 %7 16.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: 16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be features that have not yet been reported. The goal of this study was to use a patient “self-phenotyping” survey to collect data directly from patients to further characterize the phenotypes of 16p13.11 microduplication syndrome. Objective: This study aimed to (1) discover self-identified phenotypes in 16p13.11 microduplication syndrome that have been underrepresented in the scientific literature and (2) demonstrate that self-phenotyping tools are valuable sources of data for the medical and scientific communities. Methods: As part of a large study to compare and evaluate patient self-phenotyping surveys, an online survey tool, Phenotypr, was developed for patients with rare disorders to self-report phenotypes. Participants with 16p13.11 microduplication syndrome were recruited through the Boston Children's Hospital 16p13.11 Registry. Either the caregiver, parent, or legal guardian of an affected child or the affected person (if aged 18 years or above) completed the survey. Results were securely transferred to a Research Electronic Data Capture database and aggregated for analysis. Results: A total of 19 participants enrolled in the study. Notably, among the 19 participants, aggression and anxiety were mentioned by 3 (16%) and 4 (21%) participants, respectively, which is an increase over the numbers in previously published literature. Additionally, among the 19 participants, 3 (16%) had asthma and 2 (11%) had other immunological disorders, both of which have not been previously described in the syndrome. Conclusions: Several phenotypes might be underrepresented in the previous 16p13.11 microduplication literature, and new possible phenotypes have been identified. Whenever possible, patients should continue to be referenced as a source of complete phenotyping data on their condition. Self-phenotyping may lead to a better understanding of the prevalence of phenotypes in genetic disorders and may identify previously unreported phenotypes. %M 33724192 %R 10.2196/21023 %U https://www.jmir.org/2021/3/e21023 %U https://doi.org/10.2196/21023 %U http://www.ncbi.nlm.nih.gov/pubmed/33724192 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e22453 %T Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development %A Santus,Enrico %A Marino,Nicola %A Cirillo,Davide %A Chersoni,Emmanuele %A Montagud,Arnau %A Santuccione Chadha,Antonella %A Valencia,Alfonso %A Hughes,Kevin %A Lindvall,Charlotta %+ Barcelona Supercomputing Center, c/Jordi Girona, 29, Barcelona, Spain, 34 934137971, davide.cirillo@bsc.es %K COVID-19 %K SARS-CoV-2 %K artificial intelligence %K personalized medicine %K precision medicine %K prevention %K monitoring %K epidemic %K literature %K public health %K pandemic %D 2021 %7 12.3.2021 %9 Viewpoint %J J Med Internet Res %G English %X Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes. %M 33560998 %R 10.2196/22453 %U https://www.jmir.org/2021/3/e22453 %U https://doi.org/10.2196/22453 %U http://www.ncbi.nlm.nih.gov/pubmed/33560998 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e23058 %T Motivation to Participate in Precision Health Research and Acceptability of Texting as a Recruitment and Intervention Strategy Among Vietnamese Americans: Qualitative Study %A Ta Park,Van %A Kim,Amber %A Cho,In Hyang %A Nam,Bora %A Nguyen,Khue %A Vuong,Quyen %A Periyakoil,Vyjeyanthi S %A Hong,Y Alicia %+ Department of Community Health Systems, School of Nursing, University of California, San Francisco, 2 Koret Way, N511S, San Francisco, CA, 94143, United States, 1 415 514 3318, van.park@ucsf.edu %K Vietnamese Americans %K texting %K precision health %K qualitative research %K mobile phone %D 2021 %7 11.3.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The largest effort undertaken in precision health research is the Precision Medicine Initiative (PMI), also known as the All of Us Research Program, which aims to include 1 million or more participants to be a part of a diverse database that can help revolutionize precision health research studies. Research participation from Asian Americans and Pacific Islanders in precision health research is, however, limited; this includes Vietnamese Americans, especially those with limited English proficiency. PMI engagement efforts with underserved communities, including members of minority populations or individuals who have experienced health disparities such as Vietnamese Americans with limited English proficiency, may help to enrich the diversity of the PMI. Objective: The aim of this study is to examine the attitudes towards and perceptions of precision health, motivations and barriers to participation in precision health research, and acceptability of SMS text messaging as a recruitment and intervention strategy among underserved Vietnamese Americans. Methods: A community sample of 37 Vietnamese Americans completed a survey and participated in one of 3 focus groups classified by age (18-30, 31-59, and ≥60 years) on topics related to precision health, participation in precision health research, texting or social media use experience, and insights on how to use text messages for recruitment and intervention. Participants were recruited via community organizations that serve Vietnamese Americans, flyers, word of mouth, and Vietnamese language radio announcements. Results: Most participants had little knowledge of precision health initially. After brief education, they had positive attitudes toward precision health, although the motivation to participate in precision health research varied by age and prior experience of research participation. The main motivators to participate included the desire for more knowledge and more representation of Vietnamese Americans in research. Participants were open to receiving text messages as part of their research participation and provided specific suggestions on the design and delivery of such messages (eg, simple, in both English and Vietnamese). Examples of barriers included misinterpretation of messages, cost (to send text messages), and preferences for different texting platforms across age groups. Conclusions: This study represents one of the first formative research studies to recruit underserved Vietnamese Americans to precision health research. It is critical to understand target communities’ motivations and barriers to participation in research. Delivering culturally appropriate text messages via age-appropriate texting and social media platforms may be an effective recruitment and intervention strategy. The next step is to develop and examine the feasibility of a culturally tailored precision health texting strategy for Vietnamese Americans. %M 33704080 %R 10.2196/23058 %U https://mhealth.jmir.org/2021/3/e23058 %U https://doi.org/10.2196/23058 %U http://www.ncbi.nlm.nih.gov/pubmed/33704080 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 1 %P e23562 %T Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization %A Xu,Xin %A Hu,Jiming %A Lyu,Xiaoguang %A Huang,He %A Cheng,Xingyu %+ Department of Cardiology, Renmin Hospital of Wuhan University, NO 238 Jiefang Road, Wuhan, 430060, China, 86 02788041911 ext 81038, huanghe1977@whu.edu.cn %K precision medicine %K interdisciplinary %K social network analysis %K co-occurrence analysis %D 2021 %7 11.1.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear. Objective: The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. Methods: A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. Results: The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications. Conclusions: This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine. %M 33427681 %R 10.2196/23562 %U http://medinform.jmir.org/2021/1/e23562/ %U https://doi.org/10.2196/23562 %U http://www.ncbi.nlm.nih.gov/pubmed/33427681 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 6 %N 2 %P e21787 %T The Need for Education and Clinical Best Practice Guidelines in the Era of Direct-to-Consumer Genomic Testing %A Myers,Madeleine %A Bloss,Cinnamon %+ University of California San Diego, 9500 Gilman Drive MC0896, Atkinson Hall, La Jolla, CA, 92093-0896, United States, 1 8585349595, cbloss@ucsd.edu %K personal genome testing %K direct-to-consumer %K primary care %K patient-physician relationship %K medical education %D 2020 %7 8.12.2020 %9 Viewpoint %J JMIR Med Educ %G English %X Many people share the results of their direct-to-consumer personal genomic testing (DTC-PGT) within the primary care setting, seeking interpretation of and counsel about the results. However, most primary care physicians (PCPs) are not trained to interpret and communicate about DTC-PGT results. New guidelines must be developed to help PCPs maximize the potential of emerging DTC-PGT technologies. %M 33289492 %R 10.2196/21787 %U http://mededu.jmir.org/2020/2/e21787/ %U https://doi.org/10.2196/21787 %U http://www.ncbi.nlm.nih.gov/pubmed/33289492 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 9 %N 11 %P e17324 %T The Precision Health and Everyday Democracy (PHED) Project: Protocol for a Transdisciplinary Collaboration on Health Equity and the Role of Health in Society %A Strange,Michael %A Nilsson,Carol %A Zdravkovic,Slobodan %A Mangrio,Elisabeth %+ Malmö Institute for Studies of Migration, Diversity & Welfare, Malmö University, Niagara, Malmö, Sweden, 46 725466824, michael.strange@mau.se %K precision health %K health care access %K health literacy %K everyday democracy %D 2020 %7 30.11.2020 %9 Protocol %J JMIR Res Protoc %G English %X Background: The project “Precision Health and Everyday Democracy” (PHED) is a transdisciplinary partnership that combines a diverse range of perspectives necessary for understanding the increasingly complex societal role played by modern health care and medical research. The term “precision health” is being increasingly used to express the need for greater awareness of environmental and genomic characteristics that may lead to divergent health outcomes between different groups within a population. Enhancing awareness of diversity has parallels with calls for “health democracy” and greater patient-public participation within health care and medical research. Approaching health care in this way goes beyond a narrow focus on the societal determinants of health, since it requires considering health as a deliberative space, which occurs often at the banal or everyday level. As an initial empirical focus, PHED is directed toward the health needs of marginalized migrants (including refugees and asylum seekers, as well as migrants with temporary residency, often involving a legally or economically precarious situation) as vulnerable groups that are often overlooked by health care. Developing new transdisciplinary knowledge on these groups provides the potential to enhance their wellbeing and benefit the wider society through challenging the exclusions of these groups that create pockets of extreme ill-health, which, as we see with COVID-19, should be better understood as “acts of self-harm” for the wider negative impact on humanity. Objective: We aim to establish and identify precision health strategies, as well as promote equal access to quality health care, drawing upon knowledge gained from studying the health care of marginalized migrants. Methods: The project is based in Sweden at Malmö and Lund Universities. At the outset, the network activities do not require ethical approval where they will not involve data collection, since the purpose of PHED is to strengthen international research contacts, establish new research within precision strategies, and construct educational research activities for junior colleagues within academia. However, whenever new research is funded and started, ethical approval for that specific data collection will be sought. Results: The PHED project has been funded from January 1, 2019. Results of the transdisciplinary collaboration will be disseminated via a series of international conferences, workshops, and web-based materials. To ensure the network project advances toward applied research, a major goal of dissemination is to produce tools for applied research, including information to enhance health accessibility for vulnerable communities, such as marginalized migrant populations in Sweden. Conclusions: There is a need to identify tools to enable the prevention and treatment of a wide spectrum of health-related outcomes and their link to social as well as environmental issues. There is also a need to identify and investigate barriers to precision health based on democratic principles. International Registered Report Identifier (IRRID): DERR1-10.2196/17324 %M 33252352 %R 10.2196/17324 %U http://www.researchprotocols.org/2020/11/e17324/ %U https://doi.org/10.2196/17324 %U http://www.ncbi.nlm.nih.gov/pubmed/33252352 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 10 %P e13567 %T How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach %A Bhavnani,Suresh K %A Dang,Bryant %A Penton,Rebekah %A Visweswaran,Shyam %A Bassler,Kevin E %A Chen,Tianlong %A Raji,Mukaila %A Divekar,Rohit %A Zuhour,Raed %A Karmarkar,Amol %A Kuo,Yong-Fang %A Ottenbacher,Kenneth J %+ Preventive Medicine and Population Health, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555-0129, United States, 1 409 772 1928, subhavna@utmb.edu %K unplanned hospital readmission %K visual analytics %K bipartite networks %K precision medicine %D 2020 %7 26.10.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. Objective: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. Methods: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. Results: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. Conclusions: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups. %M 33103657 %R 10.2196/13567 %U https://medinform.jmir.org/2020/10/e13567 %U https://doi.org/10.2196/13567 %U http://www.ncbi.nlm.nih.gov/pubmed/33103657 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 10 %P e20291 %T Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma %A Kang,Hongyu %A Li,Jiao %A Wu,Meng %A Shen,Liu %A Hou,Li %+ Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, 3 Yabao Road, Chaoyang District, Beijing , China, 86 18910120178, hou.li@imicams.ac.cn %K pharmacogenomics %K knowledge model %K BERT–CRF model %K named entity recognition %K melanoma %D 2020 %7 21.10.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on. Objective: We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication. Methods: PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail. Results: In entity recognition tasks, the Bidirectional Encoder Representations from Transformers–conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available. Conclusions: We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data. %M 33084582 %R 10.2196/20291 %U http://medinform.jmir.org/2020/10/e20291/ %U https://doi.org/10.2196/20291 %U http://www.ncbi.nlm.nih.gov/pubmed/33084582 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 10 %P e20265 %T Clinical Decision Support May Link Multiple Domains to Improve Patient Care: Viewpoint %A Kao,David %A Larson,Cynthia %A Fletcher,Dana %A Stegner,Kris %+ Department of Cardiology, University of Colorado School of Medicine, 12700 East 19th Avenue, Aurora, CO, 80045, United States, 1 720 848 5300, david.kao@cuanschutz.edu %K clinical decision support %K population medicine %K evidence-based medicine %K precision medicine %K care management %K electronic health records %D 2020 %7 16.10.2020 %9 Viewpoint %J JMIR Med Inform %G English %X Integrating clinical decision support (CDS) across the continuum of population-, encounter-, and precision-level care domains may improve hospital and clinic workflow efficiency. Due to the diversity and volume of electronic health record data, complexity of medical and operational knowledge, and specifics of target user workflows, the development and implementation of comprehensive CDS is challenging. Additionally, many providers have an incomplete understanding of the full capabilities of current CDS to potentially improve the quality and efficiency of care delivery. These varied requirements necessitate a multidisciplinary team approach to CDS development for successful integration. Here, we present a practical overview of current and evolving applications of CDS approaches in a large academic setting and discuss the successes and challenges. We demonstrate that implementing CDS tools in the context of linked population-, encounter-, and precision-level care provides an opportunity to integrate complex algorithms at each level into a unified mechanism to improve patient management. %M 33064106 %R 10.2196/20265 %U https://medinform.jmir.org/2020/10/e20265 %U https://doi.org/10.2196/20265 %U http://www.ncbi.nlm.nih.gov/pubmed/33064106 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e19040 %T Interactive Web-Based Resource for Annotation of Genetic Variants Causing Hereditary Angioedema (HADA): Database Development, Implementation, and Validation %A Mendoza-Alvarez,Alejandro %A Muñoz-Barrera,Adrián %A Rubio-Rodríguez,Luis Alberto %A Marcelino-Rodriguez,Itahisa %A Corrales,Almudena %A Iñigo-Campos,Antonio %A Callero,Ariel %A Perez-Rodriguez,Eva %A Garcia-Robaina,Jose Carlos %A González-Montelongo,Rafaela %A Lorenzo-Salazar,Jose Miguel %A Flores,Carlos %+ Research Unit, Hospital Universitario Nuestra Señora de Candelaria, Universidad de La Laguna, Carretera del Rosario s/n, Santa Cruz de Tenerife, 38010, Spain, 34 922602938, cflores@ull.edu.es %K genetic cause %K hereditary angioedema %K knowledge database %K precision medicine %K variant interpretation %D 2020 %7 9.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Hereditary angioedema is a rare genetic condition caused by C1 esterase inhibitor deficiency, dysfunction, or kinin cascade dysregulation, leading to an increased bradykinin plasma concentration. Hereditary angioedema is a poorly recognized clinical entity and is very often misdiagnosed as a histaminergic angioedema. Despite its genetic nature, first-line genetic screening is not integrated in routine diagnosis. Consequently, a delay in the diagnosis, and inaccurate or incomplete diagnosis and treatment of hereditary angioedema are common. Objective: In agreement with recent recommendations from the International Consensus on the Use of Genetics in the Management of Hereditary Angioedema, to facilitate the clinical diagnosis and adapt it to the paradigm of precision medicine and next-generation sequencing–based genetic tests, we aimed to develop a genetic annotation tool, termed Hereditary Angioedema Database Annotation (HADA). Methods: HADA is built on top of a database of known variants affecting function, including precomputed pathogenic assessment of each variant and a ranked classification according to the current guidelines from the American College of Medical Genetics and Genomics. Results: HADA is provided as a freely accessible, user-friendly web-based interface with versatility for the entry of genetic information. The underlying database can also be incorporated into automated command-line stand-alone annotation tools. Conclusions: HADA can achieve the rapid detection of variants affecting function for different hereditary angioedema types, and further integrates useful information to reduce the diagnosis odyssey and improve its delay. %M 33034563 %R 10.2196/19040 %U http://www.jmir.org/2020/10/e19040/ %U https://doi.org/10.2196/19040 %U http://www.ncbi.nlm.nih.gov/pubmed/33034563 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e18044 %T Data Heterogeneity: The Enzyme to Catalyze Translational Bioinformatics? %A Cahan,Eli M %A Khatri,Purvesh %+ Department of Medicine, School of Medicine, Stanford University, 1265 Welch Road, Medical School Office Building, X219, Stanford, CA, 94305, United States, 1 650 497 5281, pkhatri@stanford.edu %K medical Informatics %K health equity %K health care disparities %K population health %K quality improvement %K precision medicine %D 2020 %7 12.8.2020 %9 Viewpoint %J J Med Internet Res %G English %X Up to 95% of novel interventions demonstrating significant effects at the bench fail to translate to the bedside. In recent years, the windfalls of “big data” have afforded investigators more substrate for research than ever before. However, issues with translation have persisted: although countless biomarkers for diagnostic and therapeutic targeting have been proposed, few of these generalize effectively. We assert that inadequate heterogeneity in datasets used for discovery and validation causes their nonrepresentativeness of the diversity observed in real-world patient populations. This nonrepresentativeness is contrasted with advantages rendered by the solicitation and utilization of data heterogeneity for multisystemic disease modeling. Accordingly, we propose the potential benefits of models premised on heterogeneity to promote the Institute for Healthcare Improvement’s Triple Aim. In an era of personalized medicine, these models can confer higher quality clinical care for individuals, increased access to effective care across all populations, and lower costs for the health care system. %M 32784182 %R 10.2196/18044 %U https://www.jmir.org/2020/8/e18044 %U https://doi.org/10.2196/18044 %U http://www.ncbi.nlm.nih.gov/pubmed/32784182 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e18387 %T Personal Health Information Inference Using Machine Learning on RNA Expression Data from Patients With Cancer: Algorithm Validation Study %A Kweon,Solbi %A Lee,Jeong Hoon %A Lee,Younghee %A Park,Yu Rang %+ Department of Biomedical System Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 2 2228 ext 2493, yurangpark@yuhs.ac %K cancer %K privacy issue %K personal information %K prediction %K RNA sequencing %K machine learning %D 2020 %7 10.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: As the need for sharing genomic data grows, privacy issues and concerns, such as the ethics surrounding data sharing and disclosure of personal information, are raised. Objective: The main purpose of this study was to verify whether genomic data is sufficient to predict a patient's personal information. Methods: RNA expression data and matched patient personal information were collected from 9538 patients in The Cancer Genome Atlas program. Five personal information variables (age, gender, race, cancer type, and cancer stage) were recorded for each patient. Four different machine learning algorithms (support vector machine, decision tree, random forest, and artificial neural network) were used to determine whether a patient's personal information could be accurately predicted from RNA expression data. Performance measurement of the prediction models was based on the accuracy and area under the receiver operating characteristic curve. We selected five cancer types (breast carcinoma, kidney renal clear cell carcinoma, head and neck squamous cell carcinoma, low-grade glioma, and lung adenocarcinoma) with large samples sizes to verify whether predictive accuracy would differ between them. We also validated the efficacy of our four machine learning models in analyzing normal samples from 593 cancer patients. Results: In most samples, personal information with high genetic relevance, such as gender and cancer type, could be predicted from RNA expression data alone. The prediction accuracies for gender and cancer type, which were the best models, were 0.93-0.99 and 0.78-0.94, respectively. Other aspects of personal information, such as age, race, and cancer stage, were difficult to predict from RNA expression data, with accuracies ranging from 0.0026-0.29, 0.76-0.96, and 0.45-0.79, respectively. Among the tested machine learning methods, the highest predictive accuracy was obtained using the support vector machine algorithm (mean accuracy 0.77), while the lowest accuracy was obtained using the random forest method (mean accuracy 0.65). Gender and race were predicted more accurately than other variables in the samples. On average, the accuracy of cancer stage prediction ranged between 0.71-0.67, while the age prediction accuracy ranged between 0.18-0.23 for the five cancer types. Conclusions: We attempted to predict patient information using RNA expression data. We found that some identifiers could be predicted, but most others could not. This study showed that personal information available from RNA expression data is limited and this information cannot be used to identify specific patients. %M 32773372 %R 10.2196/18387 %U https://www.jmir.org/2020/8/e18387 %U https://doi.org/10.2196/18387 %U http://www.ncbi.nlm.nih.gov/pubmed/32773372 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e15040 %T Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study %A Ryu,Borim %A Shin,Soo-Yong %A Baek,Rong-Min %A Kim,Jeong-Whun %A Heo,Eunyoung %A Kang,Inchul %A Yang,Joshua SungWoo %A Yoo,Sooyoung %+ Office of eHealth and Business, Seoul National University Bundang Hospital, 82 173rd Street, Gumi-ro, Bundang-gu, Seongnam, 136036, Republic of Korea, 82 1090537094, yoosoo0@snubh.org %K standardization %K genomics %K electronic health record %K information system %K data exchange %D 2020 %7 10.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: To implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. Objective: This study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. Methods: In this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. Results: We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. Conclusions: To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources. %M 32773368 %R 10.2196/15040 %U https://www.jmir.org/2020/8/e15040 %U https://doi.org/10.2196/15040 %U http://www.ncbi.nlm.nih.gov/pubmed/32773368 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 7 %P e16129 %T Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation %A Kasthurirathne,Suranga N %A Grannis,Shaun %A Halverson,Paul K %A Morea,Justin %A Menachemi,Nir %A Vest,Joshua R %+ Center for Biomedical Informatics, Regenstrief Institute, 1101 W 10th Street, Indianapolis, IN, 46202, United States, 1 3172749000, snkasthu@iu.edu %K social determinants of health %K supervised machine learning %K delivery of health care %K integrated %K wraparound social services %D 2020 %7 9.7.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. Objective: The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. Methods: We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. Results: Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P<.001, AUROC P=.01; social work: F1 score P<.001, AUROC P=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider. Conclusions: Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance. %M 32479414 %R 10.2196/16129 %U https://medinform.jmir.org/2020/7/e16129 %U https://doi.org/10.2196/16129 %U http://www.ncbi.nlm.nih.gov/pubmed/32479414 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e15605 %T A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development %A Schleimer,Erica %A Pearce,Jennifer %A Barnecut,Andrew %A Rowles,William %A Lizee,Antoine %A Klein,Arno %A Block,Valerie J %A Santaniello,Adam %A Renschen,Adam %A Gomez,Refujia %A Keshavan,Anisha %A Gelfand,Jeffrey M %A Henry,Roland G %A Hauser,Stephen L %A Bove,Riley %+ Department of Neurology, UCSF Weill Institute for Neurosciences, 675 Nelson Rising Lane, San Francisco, CA, 94158, United States, 1 14155952795, Riley.bove@ucsf.edu %K human-centered design %K mobile phone %K personal health record %K participatory medicine %K visualization in eHealth %K human factors %D 2020 %7 6.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. Objective: This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. Methods: We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical—many times throughout the process, we went back to the drawing board. Results: This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. Conclusions: After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions. %M 32628124 %R 10.2196/15605 %U https://www.jmir.org/2020/7/e15605 %U https://doi.org/10.2196/15605 %U http://www.ncbi.nlm.nih.gov/pubmed/32628124 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 6 %P e16886 %T Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study %A Chuang,Li-Yeh %A Yang,Cheng-San %A Yang,Huai-Shuo %A Yang,Cheng-Hong %+ Department of Electronic Engineering, National Kaohsiung University of Science and Technology, No. 415 Jiangong Road, San-Min District, Kaohsiung City, 82778, Taiwan, 886 7 381 4526, chyang@nkust.edu.tw %K genetic algorithm %K single-nucleotide polymorphism %K breast cancer %K case-control study %D 2020 %7 17.6.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci. Objective: This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients. Methods: A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability. Results: The proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes. Conclusions: We systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets. %M 32554381 %R 10.2196/16886 %U https://medinform.jmir.org/2020/6/e16886 %U https://doi.org/10.2196/16886 %U http://www.ncbi.nlm.nih.gov/pubmed/32554381 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 5 %P e16734 %T Ethical, Legal, and Social Issues Related to the Inclusion of Individuals With Intellectual Disabilities in Electronic Health Record Research: Scoping Review %A Raspa,Melissa %A Moultrie,Rebecca %A Wagner,Laura %A Edwards,Anne %A Andrews,Sara %A Frisch,Mary Katherine %A Turner-Brown,Lauren %A Wheeler,Anne %+ RTI International, 3040 East Cornwallis Road, PO Box 12194, Research Triangle Park, NC, 27709, United States, 1 19195418736, mraspa@rti.org %K electronic health records %K privacy %K informed consent %K intellectual disability %K genetics %D 2020 %7 21.5.2020 %9 Review %J J Med Internet Res %G English %X Background: Data from electronic health records (EHRs) are increasingly used in the field of genetic research to further precision medicine initiatives. However, many of these efforts exclude individuals with intellectual disabilities, which often stem from genetic conditions. To include this important subpopulation in EHR research, important ethical, legal, and social issues should be considered. Objective: The goal of this study was to review prior research to better understand what ethical, legal, and social issues may need further investigation when considering the research use of EHRs for individuals with genetic conditions that may result in intellectual disability. This information will be valuable in developing methods and best practices for involving this group in research given they are considered a vulnerable population that may need special research protections. Methods: We conducted a scoping review to examine issues related to the use of EHRs for research purposes and those more broadly associated with genetic research. The initial search yielded a total of 460 unique citations. We used an evaluative coding process to determine relevancy for inclusion. Results: This approach resulted in 59 articles in the following areas: informed consent, privacy and security, return of results, and vulnerable populations. The review included several models of garnering informed consent in EHR or genetic research, including tiered or categorical, blanket or general, open, and opt-out models. Second, studies reported on patients’ concerns regarding the privacy and security of EHR or genetic data, such as who has access, type of data use in research, identifiability, and risks associated with privacy breach. The literature on return of research results using biospecimens examined the dissension in the field, particularly when sharing individualized genetic results. Finally, work involving vulnerable populations highlighted special considerations when conducting EHR or genetic research. Conclusions: The results frame important questions for researchers to consider when designing EHR studies, which include individuals with intellectual disabilities, including appropriate safeguards and protections. %M 32436848 %R 10.2196/16734 %U http://www.jmir.org/2020/5/e16734/ %U https://doi.org/10.2196/16734 %U http://www.ncbi.nlm.nih.gov/pubmed/32436848 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e17507 %T Toward Earlier Diagnosis Using Combined eHealth Tools in Rheumatology: The Joint Pain Assessment Scoring Tool (JPAST) Project %A Knitza,Johannes %A Knevel,Rachel %A Raza,Karim %A Bruce,Tor %A Eimer,Ekaterina %A Gehring,Isabel %A Mathsson-Alm,Linda %A Poorafshar,Maryam %A Hueber,Axel J %A Schett,Georg %A Johannesson,Martina %A Catrina,Anca %A Klareskog,Lars %A , %+ Department of Internal Medicine 3 – Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, Erlangen, 91054, Germany, 49 91318532093, johannes.knitza@uk-erlangen.de %K rheumatology %K eHealth %K mHealth %K symptom-checkers %K apps %D 2020 %7 15.5.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Outcomes of patients with inflammatory rheumatic diseases have significantly improved over the last three decades, mainly due to therapeutic innovations, more timely treatment, and a recognition of the need to monitor response to treatment and to titrate treatments accordingly. Diagnostic delay remains a major challenge for all stakeholders. The combination of electronic health (eHealth) and serologic and genetic markers holds great promise to improve the current management of patients with inflammatory rheumatic diseases by speeding up access to appropriate care. The Joint Pain Assessment Scoring Tool (JPAST) project, funded by the European Union (EU) European Institute of Innovation and Technology (EIT) Health program, is a unique European project aiming to enable and accelerate personalized precision medicine for early treatment in rheumatology, ultimately also enabling prevention. The aim of the project is to facilitate these goals while at the same time, reducing cost for society and patients. %M 32348258 %R 10.2196/17507 %U https://mhealth.jmir.org/2020/5/e17507 %U https://doi.org/10.2196/17507 %U http://www.ncbi.nlm.nih.gov/pubmed/32348258 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 4 %P e14710 %T Next-Generation Sequencing–Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study %A Park,Phillip %A Shin,Soo-Yong %A Park,Seog Yun %A Yun,Jeonghee %A Shin,Chulmin %A Jung,Jipmin %A Choi,Kui Son %A Cha,Hyo Soung %+ Cancer Data Center, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang, Goyang, 10408, Republic of Korea, 82 1073502088, kkido@ncc.re.kr %K data standardization %K clinical sequencing data %K next-generation sequencing %K translational research information system %D 2020 %7 24.4.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge. Objective: To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data. Methods: We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer. Results: A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients’ clinical sequencing data were connected with clinical data in EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well. Conclusions: The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research. %M 32329738 %R 10.2196/14710 %U http://medinform.jmir.org/2020/4/e14710/ %U https://doi.org/10.2196/14710 %U http://www.ncbi.nlm.nih.gov/pubmed/32329738 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 4 %P e17366 %T Predicting Ectopic Pregnancy Using Human Chorionic Gonadotropin (hCG) Levels and Main Cause of Infertility in Women Undergoing Assisted Reproductive Treatment: Retrospective Observational Cohort Study %A Xu,Huiyu %A Feng,Guoshuang %A Wei,Yuan %A Feng,Ying %A Yang,Rui %A Wang,Liying %A Zhang,Hongxia %A Li,Rong %A Qiao,Jie %+ Peking University Third Hospital, 49 Huayuan North, Haidian District, Beijing, , China, 86 108 226 6836, roseli001@sina.com %K β-hCG %K ectopic pregnancy %K intrauterine pregnancy %K biochemical pregnancies %K IVF/ICSI-ET %D 2020 %7 16.4.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Ectopic pregnancy (EP) is a serious complication of assisted reproductive technology (ART). However, there is no acknowledged mathematical model for predicting EP in the ART population. Objective: The goal of the research was to establish a model to tailor treatment for women with a higher risk of EP. Methods: From December 2015 to July 2016, we retrospectively included 1703 women whose serum human chorionic gonadotropin (hCG) levels were positive on day 21 (hCG21) after fresh embryo transfer. Multivariable multinomial logistic regression was used to predict EP, intrauterine pregnancy (IUP), and biochemical pregnancy (BCP). Results: The variables included in the final predicting model were (hCG21, ratio of hCG21/hCG14, and main cause of infertility). During evaluation of the model, the areas under the receiver operating curve for IUP, EP, and BCP were 0.978, 0.962, and 0.999, respectively, in the training set, and 0.963, 0.942, and 0.996, respectively, in the validation set. The misclassification rates were 0.038 and 0.045, respectively, in the training and validation sets. Our model classified the whole in vitro fertilization/intracytoplasmic sperm injection–embryo transfer population into four groups: first, the low-risk EP group, with incidence of EP of 0.52% (0.23%-1.03%); second, a predicted BCP group, with incidence of EP of 5.79% (1.21%-15.95%); third, a predicted undetermined group, with incidence of EP of 28.32% (21.10%-35.53%), and fourth, a predicted high-risk EP group, with incidence of EP of 64.11% (47.22%-78.81%). Conclusions: We have established a model to sort the women undergoing ART into four groups according to their incidence of EP in order to reduce the medical resources spent on women with low-risk EP and provide targeted tailor-made treatment for women with a higher risk of EP. %M 32297865 %R 10.2196/17366 %U http://medinform.jmir.org/2020/4/e17366/ %U https://doi.org/10.2196/17366 %U http://www.ncbi.nlm.nih.gov/pubmed/32297865 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 3 %P e16770 %T Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper %A Fagherazzi,Guy %+ Luxembourg Institute of Health, Department of Population Health, Digital Epidemiology Hub, 1 A-B Rue Thomas Edison, Strassen, 1445, Luxembourg, 33 669396334, guy.fagherazzi@lih.lu %K digital health %K digital epidemiology %K deep digital phenotyping %K digital orthodoxy %K precision medicine %K precision health %K personalized medicine %K digital phenotyping %K precision prevention %K big data %K omics %K digitosome %K data lake %K digital cohort %D 2020 %7 3.3.2020 %9 Viewpoint %J J Med Internet Res %G English %X This viewpoint describes the urgent need for more large-scale, deep digital phenotyping to advance toward precision health. It describes why and how to combine real-world digital data with clinical data and omics features to identify someone’s digital twin, and how to finally enter the era of patient-centered care and modify the way we view disease management and prevention. %M 32130138 %R 10.2196/16770 %U https://www.jmir.org/2020/3/e16770 %U https://doi.org/10.2196/16770 %U http://www.ncbi.nlm.nih.gov/pubmed/32130138 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 2 %P e11287 %T Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis %A Lyu,Xiaoguang %A Hu,Jiming %A Dong,Weiguo %A Xu,Xin %+ School of Information Management, Wuhan University, Wuchang, Wuhan, 430072, China, 86 18995621959, hujiming@whu.edu.cn %K precision medicine %K topics distribution %K correlation structure %K evolution patterns %K coword analysis %D 2020 %7 4.2.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics. Objective: This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods. Methods: The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities. Results: The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped. Conclusions: The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians. %M 32014844 %R 10.2196/11287 %U https://medinform.jmir.org/2020/2/e11287 %U https://doi.org/10.2196/11287 %U http://www.ncbi.nlm.nih.gov/pubmed/32014844 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 4 %P e13043 %T Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform %A McPadden,Jacob %A Durant,Thomas JS %A Bunch,Dustin R %A Coppi,Andreas %A Price,Nathaniel %A Rodgerson,Kris %A Torre Jr,Charles J %A Byron,William %A Hsiao,Allen L %A Krumholz,Harlan M %A Schulz,Wade L %+ Department of Laboratory Medicine, Yale University School of Medicine, 55 Park Street PS345D, New Haven, CT, 06511, United States, 1 (203) 819 8609, wade.schulz@yale.edu %K data science %K monitoring, physiologic %K computational health care %K medical informatics computing %K big data %D 2019 %7 09.04.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. Objective: The objectives of our study were to (1) demonstrate the implementation of a data science platform built on open source technology within a large, academic health care system and (2) describe 2 computational health care applications built on such a platform. Methods: We deployed a data science platform based on several open source technologies to support real-time, big data workloads. We developed data-acquisition workflows for Apache Storm and NiFi in Java and Python to capture patient monitoring and laboratory data for downstream analytics. Results: Emerging data management approaches, along with open source technologies such as Hadoop, can be used to create integrated data lakes to store large, real-time datasets. This infrastructure also provides a robust analytics platform where health care and biomedical research data can be analyzed in near real time for precision medicine and computational health care use cases. Conclusions: The implementation and use of integrated data science platforms offer organizations the opportunity to combine traditional datasets, including data from the electronic health record, with emerging big data sources, such as continuous patient monitoring and real-time laboratory results. These platforms can enable cost-effective and scalable analytics for the information that will be key to the delivery of precision medicine initiatives. Organizations that can take advantage of the technical advances found in data science platforms will have the opportunity to provide comprehensive access to health care data for computational health care and precision medicine research. %M 30964441 %R 10.2196/13043 %U https://www.jmir.org/2019/4/e13043/ %U https://doi.org/10.2196/13043 %U http://www.ncbi.nlm.nih.gov/pubmed/30964441 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 1 %N 1 %P e1 %T A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives %A Elgendi,Mohamed %A Howard,Newton %A Lovell,Nigel %A Cichocki,Andrzej %A Brearley,Matt %A Abbott,Derek %A Adatia,Ian %+ Department of Obstetrics & Gynecology, University of British Columbia, BC Children's Hospital, C424, 4480 Oak Street, Vancouver, BC, V6H 3N1, Canada, 1 604 600 4139, moe.elgendi@gmail.com %K mobile health %K smart healthcare %K affordable diagnostics %K wearable devices %K global health %K eHealth %K mHealth %K point-of-care devices %D 2016 %7 17.10.2016 %9 Viewpoint %J JMIR Biomed Eng %G English %X Low- and middle-income countries (LMICs) continue to face major challenges in providing high-quality and universally accessible health care. Researchers, policy makers, donors, and program implementers consistently strive to develop and provide innovative approaches to eliminate geographical and financial barriers to health care access. Recently, interest has increased in using mobile health (mHealth) as a potential solution to overcome barriers to improving health care in LMICs. Moreover, with use increasing and cost decreasing for mobile phones and Internet, mHealth solutions are becoming considerably more promising and efficient. As part of mHealth solutions, biomedical signals collection and processing may play a major role in improving global health care. Information extracted from biomedical signals might increase diagnostic precision while augmenting the robustness of health care workers’ clinical decision making. This paper presents a high-level framework using biomedical signal processing (BSP) for tackling diagnosis of noncommunicable diseases, especially in LMICs. Researchers can consider each of these elements during the research and design of BSP-based devices, enabling them to elevate their work to a level that extends beyond the scope of a particular application and use. This paper includes technical examples to emphasize the applicability of the proposed framework, which is relevant to a wide variety of stakeholders, including researchers, policy makers, clinicians, computer scientists, and engineers. %R 10.2196/biomedeng.6401 %U http://biomedeng.jmir.org/2016/1/e1/ %U https://doi.org/10.2196/biomedeng.6401 %0 Journal Article %@ 1929-073X %I JMIR Publications Inc. %V 5 %N 2 %P e13 %T Information Needs in the Precision Medicine Era: How Genetics Home Reference Can Help %A Collins,Heather %A Calvo,Sherri %A Greenberg,Kathleen %A Forman Neall,Lisa %A Morrison,Stephanie %+ ICF International, 9300 Lee Highway, Fairfax, VA, 22031, United States, 1 301 496 0433, Heather.Collins@icfi.com %K individualized medicine %K patient education as topic %K databases, genetic %K health resources %D 2016 %7 27.04.2016 %9 Viewpoint %J Interact J Med Res %G English %X Precision medicine focuses on understanding individual variability in disease prevention, care, and treatment. The Precision Medicine Initiative, launched by President Obama in early 2015, aims to bring this approach to all areas of health care. However, few consumer-friendly resources exist for the public to learn about precision medicine and the conditions that could be affected by this approach to care. Genetics Home Reference, a website from the US National Library of Medicine, seeks to support precision medicine education by providing the public with summaries of genetic conditions and their associated genes, as well as information about issues related to precision medicine such as disease risk and pharmacogenomics. With the advance of precision medicine, consumer-focused resources like Genetics Home Reference can be foundational in providing context for public understanding of the increasing amount of data that will become available. %M 27122232 %R 10.2196/ijmr.5199 %U http://www.i-jmr.org/2016/2/e13/ %U https://doi.org/10.2196/ijmr.5199 %U http://www.ncbi.nlm.nih.gov/pubmed/27122232