Gazit Tomer, Mann Hanan, Gaber Shiri, Adamenko Pavel, Pariente Granit, Volsky Liron, Dolev Amir, Lyson Helena, Zimlichman Eyal, Pandit Jay A, Paz Edo
Hello Heart, Inc., Menlo Park, CA, United States.
Sheba Medical Center, Tel Hashomer, Israel.
Front Digit Health. 2024 Nov 1;6:1485508. doi: 10.3389/fdgth.2024.1485508. eCollection 2024.
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.
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.
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).
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.
目前的动脉粥样硬化性心血管疾病（ASCVD）风险评估工具，如合并队列方程（PCEs）和PREVENT™评分，可提供长期预测，但可能无法有效推动行为改变。利用移动健康（mHealth）数据和电子健康记录（EHRs）进行短期风险预测，可加强临床决策和患者参与度。本研究的目的是使用mHealth和EHR数据，为高血压患者开发一个短期ASCVD风险预测模型，并将其性能与现有的风险评估工具进行比较。
这是一项回顾性队列研究，纳入了2015年1月至2024年1月期间参加Hello Heart心血管风险自我管理项目的51127名年龄≥18岁的高血压参与者。通过家庭血压监测仪收集的EHR数据以及血压（BP）和心率（HR）的mHealth测量值，得出一个机器学习（ML）模型。将其性能与PCE和PREVENT的性能进行比较。
在两个预测期内，纳入291个特征的XgBoost模型在区分ASCVD风险方面均优于PCE和PREVENT评分。对于90天预测，平均C统计量分别为0.81（XgBoost）、0.74（PCE）和0.65（PREVENT）。365天预测也观察到类似结果。mHealth测量值可逐步增强365天风险预测（有mHealth时的受试者工作特征曲线下面积为0.82，无mHealth时为0.80）。
与传统工具相比，基于EHR和mHealth的ML模型提供了更优短期ASCVD预测。这种方法支持个性化预防策略，特别是对于PCE或PREVENT特征不完整的人群。进一步研究应探索这种新型风险预测框架，尤其是整合更多mHealth数据，以实现更广泛的适用性和更高的预测能力。
