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Skip search results from other journals and go to results- 10 Journal of Medical Internet Research
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However, reperfusion and medications are unable to replenish necrotic cardiac myocytes, and many patients still experience significant morbidity and mortality following acute MI [4]. Following significant tissue infarction, large areas of the myocardium are scarred and rendered nonfunctional, leading to the adoption of regenerative therapies as a possible solution.
Accordingly, regenerative therapies that aim to restore functional cardiac tissue continue to be a topic of clinical research interest.
JMIR Res Protoc 2025;14:e60591
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This includes evaluating preexisting cardiac conditions, performing risk assessment with tools such as the Revised Cardiac Risk Index (RCRI), and using an algorithm to determine if a stress test is indicated [3]. The American College of Cardiology /American Heart Association (ACC/AHA) Perioperative Cardiac Evaluation 2014 Guideline [4] provides a widely accepted preoperative evaluation algorithm.
JMIR Perioper Med 2024;7:e63076
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Here, we evaluate algorithmic inequity in ML algorithms used for predicting cardiac disease, focusing on heart failure (HF).
HF is a clinical syndrome in which the heart is unable to maintain a cardiac output adequate to meet the metabolic demands of the body [9]. Traditionally, algorithmic tools capable of identifying at-risk patients have played a key role in informing decisions on HF management and end-of-life care [10-12].
J Med Internet Res 2024;26:e46936
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This could potentially be used to track the location of patients experiencing cardiac arrest. PPG is used to detect changes in light absorption due to pulsatile blood flow [12] and hence can be used to measure the heartbeat, allowing smartwatches to accurately detect cardiac arrhythmias [13-15]. PPG and other sensors integrated in smartwatches could also potentially be used to detect cardiac arrest by measuring the cessation of pulsatile blood flow.
JMIR Hum Factors 2024;11:e57574
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Measures of cardiac structure or function, such as the output of electrocardiograms, echocardiography, or cardiac imaging, were not available in the data set, and therefore were not included in the prediction models.
The study population was randomly split with a 70:30 ratio into a training and a testing set for modeling. Using individuals from the study population, 4 models were built for each end point: a logistic regression model, a stepwise regression, an elastic net, and a random forest (RF) model.
JMIR Cardio 2024;8:e54994
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The journey of patients who underwent cardiac surgery through the hospital system begins prior to and on admission and is facilitated by a multidisciplinary team, including cardiac nurses, who educate patients about what can be expected during their ICU and cardiac ward admission.
JMIR Nursing 2024;7:e54317
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Patients with prefrailty may benefit from interventions (eg, cardiac rehabilitation) that improve frailty status and cardiovascular outcomes [1]. These findings enrich our understanding of the importance of FT in patients at lower FI levels, and a previous study compared the importance of FIs to that of FTs alone [5]. These results may not generalize to nonveteran populations. The sample was predominately male but did include a diverse population in terms of race, ethnicity, and geographic distribution.
JMIR Aging 2024;7:e56345
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LVEF, the percentage of blood in the left ventricle that exits into the aorta during a cardiac cycle, is determined using various imaging techniques, such as echocardiography, cardiac magnetic resonance imaging, nuclear cardiology, or cardiac catheterization [1,4,5]. Thus, the classification of HF depends on the accurate determination of LVEF using expensive diagnostic methods obtained in outpatient or inpatient settings [6,7].
JMIR Cardio 2024;8:e57111
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