Key result
An EHR and mHealth-based machine learning model outperformed the Pooled Cohort Equations in predicting 90-day ASCVD risk (C-statistic 0.81 vs 0.74).
Why the study?
Existing ASCVD risk assessment tools like PCE and PREVENT provide long-term predictions that may not effectively drive behavior change, highlighting the need for short-term risk models using mHealth and EHR data.
Does a machine learning model using EHR and mHealth data improve short-term ASCVD risk prediction compared to PCE and PREVENT scores in hypertensive individuals?
Cohort (n=51,127)
Does a machine learning model using EHR and mHealth data improve short-term ASCVD risk prediction compared to PCE and PREVENT scores in hypertensive individuals?
A machine learning model integrating EHR and mHealth data provides superior short-term (90- and 365-day) ASCVD risk prediction compared to traditional PCE and PREVENT scores in hypertensive patients.
Answers are grounded in this paper and cite their sources.
May aid short-term ASCVD risk assessment in hypertension via mHealth; hypothesis-generating and requires prospective validation before practice change.
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 January 2024. A machine learning (ML) model was derived from EHR data and mHealth measurements of blood pressure (BP) and heart rate (HR) collected via at-home BP monitors. Its performance was compared to that of PCE and PREVENT. Results: The XgBoost model incorporating 291 features outperformed the PCE and PREVENT scores in discriminating ASCVD risk for both prediction periods. For 90-day prediction, mean C-statistics were 0.81 (XgBoost) vs. 0.74 (PCE) and 0.65 (PREVENT). Similar findings were observed for 365-day prediction. mHealth measurements incrementally enhanced 365-day risk prediction (ROC-AUC 0.82 vs. 0.80 without mHealth). Conclusion: An EHR and mHealth-based ML model offers superior short-term ASCVD prediction compared to traditional tools. This approach supports personalized preventive strategies, particularly for populations with incomplete features for PCE or PREVENT. Further research should explore this novel risk prediction framework, and particularly additional mHealth data integration for broader applicability and increased predictive power.
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Gazit et al. (2024) conducted a cohort in Hypertension (n=51,127). EHR and mHealth-based machine learning model vs. Pooled Cohort Equations (PCE) and PREVENT scores was evaluated on 90-day ASCVD risk prediction (C-statistic) (dAUC 0.07, 95% CI 0.03-0.1, p=<0.05). An EHR and mHealth-based machine learning model outperformed the Pooled Cohort Equations in predicting 90-day ASCVD risk (C-statistic 0.81 vs 0.74).
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