一个简单且易于实施的风险模型，用于预测中国房颤患者的1年缺血性卒中和全身性栓塞。
A simple and easily implemented risk model to predict 1-year ischemic stroke and systemic embolism in Chinese patients with atrial fibrillation.
作者信息
Jiang Chao, Chen Tian-Ge, Du Xin, Li Xiang, He Liu, Lai Yi-Wei, Xia Shi-Jun, Liu Rong, Hu Yi-Ying, Li Ying-Xue, Jiang Chen-Xi, Liu Nian, Tang Ri-Bo, Bai Rong, Sang Cai-Hua, Long De-Yong, Xie Guo-Tong, Dong Jian-Zeng, Ma Chang-Sheng
机构信息
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine for Cardiovascular Diseases, Beijing 100029, China.
Ping An Health Technology, Beijing 100035, China.
出版信息
Chin Med J (Engl). 2021 May 25;134(19):2293-2298. doi: 10.1097/CM9.0000000000001515.
BACKGROUND
Accurate prediction of ischemic stroke is required for deciding anticoagulation use in patients with atrial fibrillation (AF). Even though only 6% to 8% of AF patients die from stroke, about 90% are indicated for anticoagulants according to the current AF management guidelines. Therefore, we aimed to develop an accurate and easy-to-use new risk model for 1-year thromboembolic events (TEs) in Chinese AF patients.
METHODS
From the prospective China Atrial Fibrillation Registry cohort study, we identified 6601 AF patients who were not treated with anticoagulation or ablation at baseline. We selected the most important variables by the extreme gradient boosting (XGBoost) algorithm and developed a simplified risk model for predicting 1-year TEs. The novel risk score was internally validated using bootstrapping with 1000 replicates and compared with the CHA2DS2-VA score (excluding female sex from the CHA2DS2-VASc score).
RESULTS
Up to the follow-up of 1 year, 163 TEs (ischemic stroke or systemic embolism) occurred. Using the XGBoost algorithm, we selected the three most important variables (congestive heart failure or left ventricular dysfunction, age, and prior stroke, abbreviated as CAS model) to predict 1-year TE risk. We trained a multivariate Cox regression model and assigned point scores proportional to model coefficients. The CAS scheme classified 30.8% (2033/6601) of the patients as low risk for TE (CAS score = 0), with a corresponding 1-year TE risk of 0.81% (95% confidence interval [CI]: 0.41%-1.19%). In our cohort, the C-statistic of CAS model was 0.69 (95% CI: 0.65-0.73), higher than that of CHA2DS2-VA score (0.66, 95% CI: 0.62-0.70, Z = 2.01, P = 0.045). The overall net reclassification improvement from CHA2DS2-VA categories (low = 0/high ≥1) to CAS categories (low = 0/high ≥1) was 12.2% (95% CI: 8.7%-15.7%).
CONCLUSION
In Chinese AF patients, a novel and simple CAS risk model better predicted 1-year TEs than the widely-used CHA2DS2-VA risk score and identified a large proportion of patients with low risk of TEs, which could potentially improve anticoagulation decision-making.
TRIAL REGISTRATION
www.chictr.org.cn (Unique identifier No. ChiCTR-OCH-13003729).
背景
对于决定房颤（AF）患者是否使用抗凝治疗而言，准确预测缺血性卒中至关重要。尽管仅有6%至8%的房颤患者死于卒中，但根据当前房颤管理指南，约90%的患者需使用抗凝剂。因此，我们旨在为中国房颤患者开发一种准确且易于使用的1年血栓栓塞事件（TEs）新风险模型。
方法
在前瞻性中国房颤注册队列研究中，我们纳入了6601例基线时未接受抗凝治疗或消融治疗的房颤患者。我们通过极端梯度提升（XGBoost）算法选择了最重要的变量，并开发了一种用于预测1年TEs的简化风险模型。使用1000次重复抽样的自助法对新风险评分进行内部验证，并与CHA2DS2-VA评分（将女性从CHA2DS2-VASc评分中排除）进行比较。
结果
至随访1年时，发生了163例TEs（缺血性卒中或全身性栓塞）。使用XGBoost算法，我们选择了三个最重要的变量（充血性心力衰竭或左心室功能障碍、年龄和既往卒中，简称为CAS模型）来预测1年TE风险。我们训练了一个多变量Cox回归模型，并根据模型系数赋予相应的分数。CAS方案将30.8%（2033/6601）的患者归类为TE低风险（CAS评分 = 0），相应的1年TE风险为0.81%（95%置信区间[CI]：0.41% - 1.19%）。在我们的队列中，CAS模型的C统计量为0.69（95%CI：0.65 - 0.73），高于CHA2DS2-VA评分（0.66，95%CI：0.62 - 0.70，Z = 2.01，P = 0.045）。从CHA2DS2-VA分类（低 = 0/高≥1）到CAS分类（低 = 0/高≥1）的总体净重新分类改善为12.2%（95%CI：8.7% - 15.7%）。
结论
在中国房颤患者中，一种新颖且简单的CAS风险模型在预测1年TEs方面优于广泛使用的CHA2DS2-VA风险评分，并识别出了很大一部分TEs低风险患者，这可能会改善抗凝决策。
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