Chen Jiana, Lv Meina, Xu Wenlin, Zhang Feilong, Huang Nianxu, Chen Xia, Zhang Wang, Hu Wei, Su Jun, Dai Hengfen, Gu Ping, Huang Xiaohong, Du Xiaoming, Li Ruijuan, Zheng Qiaowei, Lin Xiangsheng, Zhang Yanxia, Liu Yuxin, Zhang Min, Liu Xiumei, Zhu Zhu, Sun Jianjun, Zhang Jinhua
Department of Pharmacy, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou, China.
Int J Cardiol. 2023 Apr 1;376:56-61. doi: 10.1016/j.ijcard.2023.02.017. Epub 2023 Feb 13.
Our aim was to identify factors associated with major bleeding in patients with atrial fibrillation (AF) on direct oral anticoagulants (DOACs) and to construct and externally validate a predictive model that would provide a validated tool for clinical assessment of major bleeding.
In the development cohort, prediction model was built by logistic regression, the area under the curve (AUC), and Nomogram. External validation, analytical identification and calibration of the model using AUC, calibration curves and Hosmer-Lemeshow test.
The development cohort consisted of 4209 patients from 7 centers and the external validation cohort consisted of 1800 patients from 12 centers. Multifactorial analysis showed that age > 65 years, history of bleeding, anemia, vascular disease, antiplatelet therapy/non-steroidal anti-inflammatory drugs and rivaroxaban were independent risk factors for major bleeding, and gastrointestinal protective agents was a protective factor. The Alfalfa-MB model was constructed using these seven factors (AUC = 0.807), and in the external validation cohort, the model showed good discriminatory power (AUC = 0.743) and good calibration (Hosmer-Lemeshow test P value of 0.205). The predictive power of the six bleeding scores was ORBIT (AUC = 0.706), HAS-BLED (AUC = 0.648), ATRIA (AUC = 0.645), HEMORR2 HAGES (AUC = 0.632), ABC (AUC = 0.619) and Shireman (AUC = 0.599) in descending order.
Based on 7 factors, we derived and externally validated a predictive model for major bleeding with DOACs in patients with AF (Alfalfa-MB). The model has good predictive value and may be an effective tool to help reduce the occurrence of major bleeding in patients with DOACs.
我们的目标是确定接受直接口服抗凝剂（DOACs）治疗的心房颤动（AF）患者发生大出血的相关因素，并构建和外部验证一个预测模型，该模型将为大出血的临床评估提供一个经过验证的工具。
在开发队列中，通过逻辑回归、曲线下面积（AUC）和列线图构建预测模型。使用AUC、校准曲线和Hosmer-Lemeshow检验对模型进行外部验证、分析识别和校准。
开发队列包括来自7个中心的4209例患者，外部验证队列包括来自12个中心的1800例患者。多因素分析显示，年龄>65岁、出血史、贫血、血管疾病、抗血小板治疗/非甾体抗炎药以及利伐沙班是大出血的独立危险因素，而胃肠道保护剂是一个保护因素。使用这七个因素构建了苜蓿大出血（Alfalfa-MB）模型（AUC = 0.807），在外部验证队列中，该模型显示出良好的区分能力（AUC = 0.743）和良好的校准（Hosmer-Lemeshow检验P值为0.205）。六个出血评分的预测能力从高到低依次为ORBIT（AUC = 0.706）、HAS-BLED（AUC = 0.648）、ATRIA（AUC = 0.645）、HEMORR2 HAGES（AUC = 0.632）、ABC（AUC = 0.
