Wang Xuan, Meng Longyan, Zhao Yanxin, Liu Xueyuan
Department of Neurology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Front Neurol. 2023 Jan 18;13:1034350. doi: 10.3389/fneur.2022.1034350. eCollection 2022.
Currently, the risk of occult atrial fibrillation (AF) could not be predicted in patients with acute ischemic stroke (AIS) using a simple scoring system. Therefore, in this study, we developed and externally validated a nomogram to predict occult AF in patients with AIS.
In this study, we prospectively conducted a development cohort study with data collected at our stroke center from July 2017 to February 2018, and an external validation cohort from March 2019 to December 2019.
Follow-up data were collected from 177 participants (56.5% older than 65 years, 29.4% female) for generating the nomogram model. Multivariate logistic regression analysis was performed with AF as the dependent variable indicated that age >65 years, heart rate >100, C-reactive protein (CRP), N-terminal pro-B-type natriuretic peptide (NT-proBNP) >270, hemorrhagic transformation (HT) as independent variables for predicting the development of AF, and a nomogram was generated based on these factors. The area under the receiver operating characteristic curve (AUC-ROC) for the model was 0.937, the C-index was 0.926, and the AUC-ROC for the validation cohort was 0.913.
To our knowledge, this is the first nomogram developed and externally validated in a stroke center cohort for individualized prediction of risk of developing AIS in patients with occult AF. This nomogram could provide valuable information for the screening of occult AF after a stroke.
目前,使用简单评分系统无法预测急性缺血性卒中(AIS)患者隐匿性房颤(AF)的风险。因此,在本研究中,我们开发并对外验证了一种用于预测AIS患者隐匿性AF的列线图。
在本研究中,我们前瞻性地进行了一项开发队列研究,收集了2017年7月至2018年2月在我们卒中中心的数据,以及一项2019年3月至2019年12月的外部验证队列研究。
收集了177名参与者(56.5%年龄大于65岁,29.4%为女性)的随访数据以生成列线图模型。以AF为因变量进行多变量逻辑回归分析,结果表明年龄>65岁、心率>100、C反应蛋白(CRP)、N末端B型利钠肽原(NT-proBNP)>270、出血性转化(HT)为预测AF发生的自变量,并基于这些因素生成了列线图。该模型的受试者工作特征曲线下面积(AUC-ROC)为0.937,C指数为0.926,验证队列的AUC-ROC为0.913。
据我们所知,这是首个在卒中中心队列中开发并对外验证的用于个体化预测隐匿性AF患者发生AIS风险的列线图。该列线图可为卒中后隐匿性AF的筛查提供有价值的信息。