Zhang Tingyu, Hao Zelin, Jiang Qunlian, Zhu Linhui, Ye Lifang
Department of Neurosurgery, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.
Heart Center, Department of Cardiovascular Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), No. 158 Shangtang Road, Gongshu District, Hangzhou, 310014, Zhejiang, China.
Sci Rep. 2025 Aug 20;15(1):30657. doi: 10.1038/s41598-025-10104-z.
Cerebral-cardiac syndrome (CCS) is a severe cardiac complication following acute ischemic stroke, often associated with adverse outcomes. This study developed and validated a machine learning (ML) model to predict CCS using clinical, laboratory, and pre-extracted imaging features. A retrospective cohort of 511 post-stroke patients was analyzed. Data on demographics, laboratory results, imaging findings, and medications were collected. CCS diagnosis was based on cardiac dysfunction occurring after stroke, excluding pre-existing cardiac diseases. Five machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Deep Neural Network, were trained on 80% of the data and tested on the remaining 20%. Discrimination was assessed by AUC (95% CI), calibration by Hosmer-Lemeshow test and Brier score, and thresholds by Youden's index. Model interpretability was evaluated using SHAP. On the test set, XGBoost achieved the highest discrimination (AUC 0.879; 95% CI 0.807-0.942), accuracy 0.825, precision 0.844, recall 0.675, and F1 score 0.750. Random forest followed closely (AUC 0.866; accuracy 0.845; precision 0.962; recall 0.625; F1 0.758). SVM and logistic regression yielded AUCs of 0.853 and 0.818, respectively. Calibration was optimal for SVM (HL p > 0.05; Brier 0.126) and random forest (HL p > 0.05; Brier 0.131). SHAP analysis identified D-dimer, ACEI/ARB use, HbA1c, C-reactive protein, and prothrombin time as top predictors. ML-based models accurately predict early CCS in ischemic stroke patients. XGBoost offers superior discrimination, while SVM and random forest demonstrate better calibration. Incorporation of these models into clinical workflows may enhance risk stratification and guide targeted preventive strategies.
脑心综合征（CCS）是急性缺血性卒中后的一种严重心脏并发症，常与不良预后相关。本研究开发并验证了一种机器学习（ML）模型，用于利用临床、实验室和预先提取的影像特征预测CCS。对511例卒中后患者的回顾性队列进行了分析。收集了人口统计学、实验室检查结果、影像检查结果和用药情况的数据。CCS诊断基于卒中后出现的心脏功能障碍，排除既往存在的心脏病。包括逻辑回归、随机森林、支持向量机（SVM）、XGBoost和深度神经网络在内的五种机器学习模型在80%的数据上进行训练，并在其余20%的数据上进行测试。通过AUC（95%CI）评估辨别力，通过Hosmer-Lemeshow检验和Brier评分评估校准情况，通过约登指数评估阈值。使用SHAP评估模型的可解释性。在测试集上，XGBoost实现了最高的辨别力（AUC 0.879；95%CI 0.807-0.942），准确率0.825，精确率0.844，召回率0.675，F1分数0.750。随机森林紧随其后（AUC 0.866；准确率0.845；精确率0.962；召回率0.625；F1 0.758）。SVM和逻辑回归的AUC分别为0.853和0.818。SVM（HL p＞0.05；Brier 0.126）和随机森林（HL p＞0.05；Brier 0.131）的校准情况最佳。SHAP分析确定D-二聚体、使用ACEI/ARB、糖化血红蛋白、C反应蛋白和凝血酶原时间为主要预测因素。基于ML的模型可准确预测缺血性卒中患者的早期CCS。XGBoost具有卓越的辨别力，而SVM和随机森林表现出更好的校准。将这些模型纳入临床工作流程可能会加强风险分层并指导有针对性的预防策略。
