中国卒中杂志2025，Vol.20Issue(4)：391-400,10.DOI:10.3969/j.issn.1673-5765.2025.04.002
基于脑影像及临床特征的机器学习模型预测缺血性卒中后心房颤动
A Machine Learning Model Based on Brain Imaging and Clinical Features for Predicting Atrial Fibrillation Detected after Stroke
摘要
Abstract
Objective To investigate the predictive value of a machine learning model based on brain imaging and clinical features in patients with atrial fibrillation detected after stroke.Methods A retrospective cohort design was used in this study.Data was derived from the ischemic stroke and TIA patients enrolled in the China national stroke registry Ⅲ from August 2015 to March 2018.Patients were divided into two groups according to the systematic collection of past medical history records,electrocardiogram,and 24-hour Holter monitoring results during hospitalization:the sinus rhythm group and the atrial fibrillation detected after stroke group.Firstly,a pre-trained nnUNet deep learning framework was applied for standardized preprocessing and automated lesion segmentation of DWI data.Subsequently,960 quantitative imaging features encompassing eight categories,including morphological characteristics,first-order statistics,and advanced texture features,were extracted using the PyRadiomics open-source package.During the feature engineering stage,the Spearman's rank correlation coefficient analysis was applied(preset threshold|ρ|>0.8)to eliminate highly collinear features.After retaining independent features,the least absolute shrinkage and selection operator(LASSO)regression algorithm was used for feature selection and to construct a joint prediction model.The model performance was internally validated via five-fold cross-validation,and the AUC of the ROC curve was used as the primary evaluation indicator.Finally,the Shapley Additive exPlanations framework was used to analyze the importance of features.Results A total of 1464 ischemic stroke patients were included,with an average age of(64.5±11.1)years,including 498 patients with atrial fibrillation detected after stroke and 966 patients with sinus rhythm.The average AUC of five-fold cross-validation of the prediction model for atrial fibrillation detected after stroke constructed using 15 clinical features was 0.71(95%CI0.67-0.74).Clinical and imaging features were fused to form 975 multimodal features,with an average AUC of 0.73(95%CI0.70-0.76).Using the LASSO algorithm for feature selection,31 multimodal features(including 25 imaging and 6 clinical features)were obtained after screening,with an average AUC of 0.73(95%CI0.70-0.77).Conclusions The machine learning model based on brain imaging and clinical features can effectively predict atrial fibrillation detected after stroke,and can be further applied in clinical practice.关键词
缺血性卒中/心房颤动/机器学习Key words
Ischemic stroke/Atrial fibrillation/Machine learning分类
临床医学引用本文复制引用
张栗源,刘涛,姜勇,李子孝,王拥军,杨晓萌..基于脑影像及临床特征的机器学习模型预测缺血性卒中后心房颤动[J].中国卒中杂志,2025,20(4):391-400,10.基金项目
国家自然科学基金青年科学基金项目(82001237)北京市医院管理中心"青苗"计划专项经费(QML20230503) （82001237）
