Key result
Transformer-based models improve 10-year CVD risk prediction discrimination by ~0.03 over traditional Cox models.
Why the study?
Traditional Cox proportional hazards models show suboptimal performance for CVD risk prediction in Chinese populations, while transformer-based deep learning models have shown clinical promise.
Does the transformer-based China-AIHeart model improve 10-year CVD risk prediction compared to traditional Cox models and established risk scores in Chinese adults?
Cohort (n=156,790)
Yes
Does the transformer-based China-AIHeart model improve 10-year CVD risk prediction compared to traditional Cox models and established risk scores in Chinese adults?
The transformer-based China-AIHeart model outperforms traditional Cox-based approaches and established risk scores for 10-year CVD risk prediction in Chinese adults.
Answers are grounded in this paper and cite their sources.
May enhance 10-year CVD risk stratification in Chinese adults; extends Cox models yet remains hypothesis-generating.
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.
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Cao et al. (2026) conducted a cohort in Cardiovascular disease (n=156,790). Transformer-based models (China-AIHeart) vs. Cox proportional hazards models and established risk scores was evaluated on 10-year cardiovascular disease risk prediction discrimination (ΔC-statistic 0.027 in men, 0.031 in women, 95% CI 0.025-0.028 in men, 0.029-0.033 in women). The transformer-based China-AIHeart models outperformed traditional Cox models for 10-year CVD risk prediction, showing improved discrimination (ΔC-statistic 0.027 in men and 0.031 in women).
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