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Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium

A paired sample t test will be performed for at least the 10-year ASCVD risk, assuming normality. Individual components, including systolic blood pressure, use of blood pressure medication, total cholesterol, HDL, use of lipid-modifying agents, smoking status, and moderate to vigorous physical activity, will be evaluated using the Mc Nemar test for dichotomous outcomes and the paired sample t test for continuous variables.

Rutger van Mierlo, Bart Scheenstra, Joost Verbeek, Anke Bruninx, Petros Kalendralis, Inigo Bermejo, Andre Dekker, Arnoud van 't Hof, Marieke Spreeuwenberg, Laura Hochstenbach

JMIR Res Protoc 2025;14:e66068