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

Autor:	Gazit T; Hello Heart, Inc., Menlo Park, CA, United States., Mann H; Hello Heart, Inc., Menlo Park, CA, United States., Gaber S; Hello Heart, Inc., Menlo Park, CA, United States., Adamenko P; Hello Heart, Inc., Menlo Park, CA, United States., Pariente G; Hello Heart, Inc., Menlo Park, CA, United States., Volsky L; Hello Heart, Inc., Menlo Park, CA, United States., Dolev A; Hello Heart, Inc., Menlo Park, CA, United States., Lyson H; Hello Heart, Inc., Menlo Park, CA, United States., Zimlichman E; Sheba Medical Center, Tel Hashomer, Israel., Pandit JA; Scripps Research Translational Institute, La Jolla, CA, United States., Paz E; Hello Heart, Inc., Menlo Park, CA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in digital health [Front Digit Health] 2024 Nov 01; Vol. 6, pp. 1485508. Date of Electronic Publication: 2024 Nov 01 (Print Publication: 2024).
DOI:	10.3389/fdgth.2024.1485508
Abstrakt:	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. Competing Interests: TG, HL, EP, HM, SG, PA, GP, LV, and AD are employed by Hello Heart and receive equity from Hello Heart. EP is also employed by White Plains Hospital. EZ is employed by Sheba Medical Center, Tel Hashomer and is an advisor for Hello Heart and receives consulting fees. JP is employed by Scripps Research Translational Institute and the Scripps Research team was funded by the National Center for Advancing Translational Sciences at the National Institutes of Health (UM1TR004407). (© 2024 Gazit, Mann, Gaber, Adamenko, Pariente, Volsky, Dolev, Lyson, Zimlichman, Pandit and Paz.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu