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Transformer-based models for predicting cardiovascular risk in Chinese adults: development and validation
Qiuyu Cao1,2, Xingkun Xu2, Hong Lin1,2
1Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Rui Jin 2nd Road, Shanghai 200025, China.
Background And Aims:
Traditional Cox proportional hazards models show suboptimal performance for cardiovascular disease (CVD) risk prediction in Chinese populations. Transformer-based deep learning models have demonstrated promise in clinical risk prediction. In this study, sex-specific transformer-based models (China-AIHeart) for 10-year CVD risk prediction among Chinese adults were developed and validated.
Methods:
The derivation cohort included 156 790 participants [34.6% men; mean [SD] age, 56.7 [8.9] years) without CVD from the China Cardiometabolic Disease and Cancer Cohort. External validation was conducted in two independent Chinese cohorts (Xinjiang and CHARLS). Transformer-based time-to-event prediction models were developed, including a full model (22 predictors) and a simplified model (15 predictors). Performance was compared with Cox models using identical predictors and established risk scores (China-PAR, PREVENT-ASCVD, and SCORE2 Asia-Pacific equations).
Results:
China-AIHeart demonstrated good discrimination (C-statistic [95% confidence interval, CI]: .767 [.754-.779] in men; .780 [.769-.791] in women), calibration (calibration χ2: 14.806 in men; 9.326 in women; Brier score: .104 in men; .077 in women), and net clinical benefit in predicting CVD risk. Predicted event rates closely matched observed risks across strata. Compared with Cox models with identical predictors, China-AIHeart showed improved discrimination (ΔC-statistic [95% CI]: .027 [.025-.028] in men; .031 [.029-.033] in women) and reclassification (net reclassification index [95% CI]: .478 [.466-.492] in men; .560 [.551-.572] in women), and outperformed China-PAR, PREVENT-ASCVD, and SCORE2 Asia-Pacific equations. External validation demonstrated robust performance, with C-statistics of .781/.825 (men/women) and .748/.820 for the full and simplified models in the Xinjiang cohort, and .740/.771 for the simplified model in the CHARLS cohort.
Conclusions:
The transformer-based China-AIHeart models predicted 10-year CVD risk and outperformed traditional Cox-based approaches, providing a practical tool for risk stratification in Chinese adults.
