Abstract 4129397: Cardiovascular Risk Models Using Large-Scale Physical Examination Data

Background: Physical examination (PE) data offer more potential key risk factors that can enhance the development of risk models. Additionally, the latest CVD risk model, PREVENT, developed from an American population, necessitates validation within China. Objectives: This study aimed to develop CVD risk models based on large-scale PE records and compare them with PREVENT model in Chinese adults. Methods: Data were collected from a China national database (2016-2024), including 929682 participants aged 30-79 years without previous CVD (Table I). The variables covered demographics, comorbidities, and PE items, resulting in 59 features as input. The endpoint events were defined as total CVD and its subtypes, ASCVD and heart failure (HF). Sex-specific survival analysis was developed based on age-scale, utilizing Cox Proportional Hazards and Random Survival Forests (RSF). Feature importance was assessed using the average minimal depth in RSF. The performance was evaluated by the Harrell C-statistic in the validation set. Results: The results showed that our RSF model achieved the best performance in total CVD, with the highest C-index of 0.82 (Table II). The top 15 predictive risk factors for each gender are presented in Figure 1. Additionally, validation of the PREVENT model in our Chinese population dataset showed its great generalizability, particularly for HF events. Conclusions: Overall, this research underscores the importance of leveraging large-scale PE data and advanced modeling techniques to improve CVD risk prediction. Beyond traditional risk factors, greater emphasis should be placed on more demographic characteristics, comorbidities, urinalysis, and ECG features.