A novel, machine-learning model for prediction of short-term ASCVD risk over 90 and 365 days.

ABSTRACT:

Background

Current atherosclerotic cardiovascular disease (ASCVD) risk assessment tools like the Pooled Cohort Equations (PCEs) and PREVENT™ scores offer long-term predictions but may not effectively drive behavior change. Short-term risk predictions using mobile health (mHealth) data and electronic health records (EHRs) could enhance clinical decision-making and patient engagement. The aim of this study was to develop a short-term ASCVD risk prediction model for hypertensive individuals using mHealth and EHR data and compare its performance to existing risk assessment tools.

Methods

This is a retrospective cohort study including 51,127 hypertensive participants aged ≥18 years old who enrolled in the Hello Heart CV risk self-management program between January 2015 and Januar

SUBMITTER: Gazit T 

PROVIDER: S-EPMC11564171 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature