Chen Yung-Lung, Wang Hui-Ting, Chen Huang-Chung, Liu Wen-Hao, Hsueh Shukai, Chung Wen-Jung, Wu Po-Jui, Liu Chi-Hung, Chung Chang-Ming, Lin Yu-Sheng
Division of Cardiology and Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine.
Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University.
Medicine (Baltimore). 2020 Jul 2;99(27):e20881. doi: 10.1097/MD.0000000000020881.
Atrial fibrillation (AF) is a major independent risk factor of stroke and anticoagulation therapy is needed in patients with AF after ischemic stroke. However, the detection rate of AF is low after ischemic stroke. Developing a prediction model for newly diagnosed AF after ischemic stroke will help to assess the subclinical AF.We identified 98,103 patients with diabetes mellitus (DM) and 261,893 patients without DM, who were not AF history and admitted for newly ischemic stroke from the National Health Insurance Research Database in Taiwan. The prediction model for 3-year incidence of AF after ischemic stroke was derived from multivariate logistic regression and also the accuracy rate of the prediction model was compared with CHA2DS2-VASC and CHADS2 scores as a reference.Four thousand nine hundred seventy six patients in the DM cohort and 16,127 patients in the non-DM cohort developed AF during 3 years of follow-up. The variables in the point-based prediction model for non-DM patients (range: -3-28), included age, heart failure, coronary artery disease, gout, obstructive pulmonary disease, hypertension, female, and statin use, while those for DM patients (range: -2-30) included age, heart failure, coronary artery disease, chronic kidney disease, hypertension, obstructive pulmonary disease, and statin use. Compared to the CHADS2 and CHA2DS2-VASc scoring systems, this scoring system was better at predicting 3-year risk of AF after ischemic stroke in both cohorts.This model might be useful in evaluating the benefit of insertable cardiac monitor implantation and anticoagulation agents in individual patients after ischemic stroke.
心房颤动（AF）是卒中的主要独立危险因素，缺血性卒中后房颤患者需要进行抗凝治疗。然而，缺血性卒中后房颤的检出率较低。建立缺血性卒中后新诊断房颤的预测模型将有助于评估亚临床房颤。我们从台湾国民健康保险研究数据库中识别出98103例糖尿病（DM）患者和261893例非糖尿病患者，这些患者均无房颤病史且因新发缺血性卒中入院。缺血性卒中后房颤3年发病率的预测模型来自多因素逻辑回归分析，并将该预测模型的准确率与CHA2DS2-VASC和CHADS2评分进行比较作为参考。糖尿病队列中的4976例患者和非糖尿病队列中的16127例患者在3年随访期间发生了房颤。非糖尿病患者基于点数的预测模型（范围：-3至28）中的变量包括年龄、心力衰竭、冠状动脉疾病、痛风、阻塞性肺疾病、高血压、女性和他汀类药物使用情况，而糖尿病患者（范围：-2至30）的变量包括年龄、心力衰竭、冠状动脉疾病、慢性肾脏疾病、高血压、阻塞性肺疾病和他汀类药物使用情况。与CHADS2和CHA2DS2-VASc评分系统相比，该评分系统在预测两个队列缺血性卒中后房颤3年风险方面表现更佳。该模型可能有助于评估缺血性卒中后个体患者植入式心脏监测器植入和抗凝药物的获益情况。
