TY  - JOUR
AU  - Glyde, Henry Mark Granger
AU  - Morgan, Caitlin
AU  - Wilkinson, Tom M A
AU  - Nabney, Ian T
AU  - Dodd, James W
PY  - 2024
DA  - 2024/9/9
TI  - Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis
JO  - J Med Internet Res
SP  - e52143
VL  - 26
KW  - acute exacerbations of COPD
KW  - chronic obstructive pulmonary disease
KW  - exacerbate
KW  - exacerbation
KW  - exacerbations
KW  - remote patient monitoring
KW  - RPM
KW  - predict
KW  - prediction
KW  - predictions
KW  - predictive
KW  - machine learning
KW  - monitoring
KW  - remote
KW  - COPD
KW  - pulmonary
KW  - respiratory
KW  - lung
KW  - lungs
KW  - literature review
KW  - literature reviews
KW  - synthesis
KW  - narrative review
KW  - narrative reviews
KW  - review methods
KW  - review methodology
AB  - Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems. Objective: This study aimed to conduct a dual systematic review. The first review focuses on randomized controlled trials where RPM was used as an intervention to treat or improve AECOPD. The second review examines studies that combined machine learning with RPM to predict AECOPD. We review the evidence and concepts behind RPM and machine learning and discuss the strengths, limitations, and clinical use of available systems. We have generated a list of recommendations needed to deliver patient and health care system benefits. Methods: A comprehensive search strategy, encompassing the Scopus and Web of Science databases, was used to identify relevant studies. A total of 2 independent reviewers (HMGG and CM) conducted study selection, data extraction, and quality assessment, with discrepancies resolved through consensus. Data synthesis involved evidence assessment using a Critical Appraisal Skills Programme checklist and a narrative synthesis. Reporting followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results: These narrative syntheses suggest that 57% (16/28) of the randomized controlled trials for RPM interventions fail to achieve the required level of evidence for better outcomes in AECOPD. However, the integration of machine learning into RPM demonstrates promise for increasing the predictive accuracy of AECOPD and, therefore, early intervention. Conclusions: This review suggests a transition toward the integration of machine learning into RPM for predicting AECOPD. We discuss particular RPM indices that have the potential to improve AECOPD prediction and highlight research gaps concerning patient factors and the maintained adoption of RPM. Furthermore, we emphasize the importance of a more comprehensive examination of patient and health care burdens associated with RPM, along with the development of practical solutions. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e52143
UR  - https://doi.org/10.2196/52143
DO  - 10.2196/52143
ID  - info:doi/10.2196/52143
ER  -