Huang Hao, Xiong Yan, Yao Yuan, Zeng Jie
Department of Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Department of Cardiology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
BMC Cardiovasc Disord. 2025 May 24;25(1):400. doi: 10.1186/s12872-025-04870-x.
Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phenotypes among elderly AF patients and to investigate more efficacious models for predicting stroke risk.
This single-center prospective cohort study collected clinical data and cardiac computed tomography angiography (CTA) images from elderly AF patients. The clinical phenotypes and left atrial appendage (LAA) radiomic phenotypes of elderly AF patients were identified through K-means clustering. The independent correlations between these phenotypes and stroke risk were subsequently analyzed. Machine learning algorithms-Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting-were selected to develop a predictive model for stroke risk in this patient cohort. The model was assessed using the Area Under the Receiver Operating Characteristic Curve, Hosmer-Lemeshow tests, and Decision Curve Analysis.
A total of 419 elderly AF patients (≥ 65 years old) were included. K-means clustering identified three clinical phenotypes: Group A (cardiac enlargement/dysfunction), Group B (normal phenotype), and Group C (metabolic/coagulation abnormalities). Stroke incidence was highest in Group A (19.3%) and Group C (14.5%) versus Group B (3.3%). Similarly, LAA radiomic phenotypes revealed elevated stroke risk in patients with enlarged LAA structure (Group B: 20.0%) and complex LAA morphology (Group C: 14.0%) compared to normal LAA (Group A: 2.9%). Among the five machine learning models, the SVM model achieved superior prediction performance (AUROC: 0.858 [95% CI: 0.830-0.887]).
The stroke-risk prediction model for elderly AF patients constructed based on the SVM algorithm has strong predictive efficacy.
心房颤动（AF）是缺血性卒中的主要病因之一，明确老年AF患者的风险表型并研究更有效的卒中风险预测模型至关重要。
这项单中心前瞻性队列研究收集了老年AF患者的临床数据和心脏计算机断层扫描血管造影（CTA）图像。通过K均值聚类确定老年AF患者的临床表型和左心耳（LAA）影像组学表型。随后分析这些表型与卒中风险之间的独立相关性。选择机器学习算法——逻辑回归、朴素贝叶斯、支持向量机（SVM）、随机森林和极限梯度提升——来建立该患者队列中卒中风险的预测模型。使用受试者操作特征曲线下面积、Hosmer-Lemeshow检验和决策曲线分析对模型进行评估。
共纳入419例老年AF患者（≥65岁）。K均值聚类确定了三种临床表型：A组（心脏扩大/功能障碍）、B组（正常表型）和C组（代谢/凝血异常）。A组（19.3%）和C组（14.5%）的卒中发生率高于B组（3.3%）。同样，与正常LAA（A组：
2.9%）相比，LAA影像组学表型显示LAA结构扩大（B组：20.0%）和LAA形态复杂（C组：14.0%）的患者卒中风险升高。在五个机器学习模型中，SVM模型具有卓越的预测性能（曲线下面积：0.858 [95%置信区间：0.830-0.887]）。
基于SVM算法构建的老年AF患者卒中风险预测模型具有较强的预测效能。
