Transformer-based models for predicting cardiovascular risk in Chinese adults: development and validation

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ID: 320233
2026
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Abstract
Abstract 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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Authors Qiuyu Cao, Xingkun Xu, Lin Hong, Yue Yin, Shengli Wu, Mian Li, Yu Xu, Shaoxin Li, Yuchen Xu, Huapeng Wei, Ruizhi Zheng, Yujing Zhu, Min Xu, Tiange Wang, Zhiyun Zhao, Yiming Mu, Lulu Chen, Tianshu Zeng, Lixin Shi, Qing Su, Xuefeng Yu, Li Yan, Guijun Qin, Qin Wan, Gang Chen, Xulei Tang, Zhengnan Gao, Ruying Hu, Zuojie Luo, Yingfen Qin, Li Chen, Xinguo Hou, Yanan Huo, Qiang Li, Guixia Wang, Yu Zhang, Chao Liu, Y Wang, Feixia Shen, Xuejiang Gu, Tao Yang, Huacong Deng, Yu Zhang, Jiajun Zhao, Guang Ning, Feiyue Huang, Weiqing Wang, Yufang Bi, Jieli Lu
Journal european heart journal
Year 2026
DOI
10.1093/eurheartj/ehag517
URL
https://doi.org/10.1093/eurheartj/ehag517
Keywords Keywords not found

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