医学
心房颤动
冲程(发动机)
比例危险模型
危险系数
内科学
心脏病学
预测建模
统计
置信区间
机械工程
工程类
数学
作者
Rungroj Krittayaphong,Wiwat Kanjanarutjawiwat,Treechada Wisaratapong,Gregory Y.H. Lip
摘要
The objectives of this study were to compare the GARFIELD Refitted model and CHA2 DS2 -VASc/HAS-BLED risk scores with the new model from the COOL-AF registry for all-cause death, ischaemic stroke/systemic embolism (SSE) and major bleeding in Asian patients with atrial fibrillation (AF).Patients with non-valvular AF in the nationwide COOL-AF registry were studied. Patients were enrolled from 27 hospitals in Thailand during 2014-2017. Main outcomes were all-cause mortality, SSE and major bleeding. Predictive models of the three outcomes were developed from the variables in the multivariable Cox-proportional Hazard model. Predictive values of the models were evaluated by C-statistics, calibration plots and decision curve analysis (DCA). The new COOL-AF models were compared with the GARFIELD Refitted models and CHA2 DS2 -VASc model for all-cause mortality, SSE/HAS-BLED model for major bleeding.A total of 3405 patients were enrolled. The C-statistics for the COOL-AF models were 0.727 (0.712-0.742), 0.708 (0.693-0.724) and 0.706 (0.690-0.721) for all-cause mortality, SSE and major bleeding, respectively. Calibration plots showed good agreement between predicted probability the observed outcomes for the COOL-AF models with a calibration slope of 0.94-0.99. The predictive ability remains preserved after the internal validation with bootstraps and optimism (bias) correction. The COOL-AF predictive models tended to be superior to the GARFIELD Refitted, CHA2 DS2 -VASc and HAS-BLED models.The COOL-AF predictive models for all-cause mortality, SSE and major bleeding in Asian patients with AF had a good predictive ability. The COOL-AF model for all-cause mortality was superior to the GARFIELD Refitted and CHA2 DS2 -VASc model.
科研通智能强力驱动
Strongly Powered by AbleSci AI