Transformer-based models for predicting cardiovascular risk in Chinese adults: development and validation.
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Cao Qiuyu, Xu Xingkun, Lin Hong, Yin Yue, Wu Shengli, Li Mian, Xu Yu, Li Shaoxin, Xu Yuchen, Wei Huapeng, Zheng Ruizhi, Zhu Yujing, Xu Min, Wang Tiange, Zhao Zhiyun, Mu Yiming, Chen Lulu, Zeng Tianshu, Shi Lixin, Su Qing, Yu Xuefeng, Yan Li, Qin Guijun, Wan Qin, Chen Gang, Tang Xulei, Gao Zhengnan, Hu Ruying, Luo Zuojie, Qin Yingfen, Chen Li, Hou Xinguo, Huo Yanan, Li Qiang, Wang Guixia, Zhang Yinfei, Liu Chao, Wang Youmin, Shen Feixia, Gu Xuejiang, Yang Tao, Deng Huacong, Zhang Yifang, Zhao Jiajun, Ning Guang, Huang Feiyue, Wang Weiqing, Bi Yufang, Lu Jieli
Аннотация
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.