Hu Xiaolong, Qi Dayong, Li Suya, Ye Shifei, Chen Yue, Cao Wei, Du Meng, Zheng Tianheng, Li Peng, Fang Yibin
Tongji University School of Medicine, Tongji University Affiliated Shanghai 4th People's Hospital, Shanghai, People's Republic of China.
Department of Neurovascular Disease, Tongji University Affiliated Shanghai 4th People's Hospital, Shanghai, People's Republic of China.
Ther Clin Risk Manag. 2025 May 3;21:621-631. doi: 10.2147/TCRM.S520362. eCollection 2025.
Despite successful recanalization after thrombectomy in patients with acute ischemic stroke, poor prognosis often persists. This study aimed to investigate the factors contributing to clinically ineffective reperfusion (CIR), develop and validate a machine-learning model to predict CIR, and provide guidance for future clinical treatments.
We collected data from patients undergoing thrombectomy at Shanghai Fourth People's Hospital between December 2021 and June 2024. The clinical variables were compared between the clinically ineffective and effective recanalization groups using univariate analysis. Four machine learning models were developed: random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN). Model performance was evaluated using receiver operating characteristic (ROC) curves and heatmap visualization. The SHAP method rank the feature importance and provided interpretability for the final model.
Among the four machine learning models, the RF model showed the best performance, with an area under the curve (AUC) of 0.96 (95% CI: 0.91-1.0), accuracy of 0.93, and specificity of 0.97 on the test dataset. The SHAP algorithm identified the number of endovascular thrombectomy (EVT) attempts as the key factor influencing CIR. Based on the RF model, a web-based calculator for CIR prediction is available at https://ineffectivereperfusion.shinyapps.io/calculate/. The final model included ten parameters: EVT attempts, diabetes mellitus, previous ischemic stroke, National Institutes of Health Stroke Scale (NIHSS score), preoperative infarction in the basal ganglia, baseline diastolic blood pressure, clot burden score (CBS)/basilar artery on computed tomography angiography (BATMAN) score, stroke cause, collateral grade, and MLS.
We developed and validated the first interpretable machine learning model for CIR prediction after EVT, surpassing traditional methods. Our CIR risk prediction platform enables early intervention and personalized treatment. The number of EVT attempts has emerged as a key determinant, underscoring the need for optimized procedural timing to improve outcomes.
尽管急性缺血性中风患者在血栓切除术后成功实现血管再通，但预后往往仍然较差。本研究旨在调查导致临床无效再灌注（CIR）的因素，开发并验证一种用于预测CIR的机器学习模型，并为未来的临床治疗提供指导。
我们收集了2021年12月至2024年6月期间在上海第四人民医院接受血栓切除术的患者的数据。使用单因素分析比较临床无效再通组和有效再通组之间的临床变量。开发了四种机器学习模型：随机森林（RF）、支持向量机（SVM）、决策树（DT）和k近邻（KNN）。使用受试者工作特征（ROC）曲线和热图可视化评估模型性能。SHAP方法对特征重要性进行排名，并为最终模型提供可解释性。
在四种机器学习模型中，RF模型表现最佳，在测试数据集上的曲线下面积（AUC）为0.96（95%CI：0.91 - 1.0），准确率为0.93，特异性为0.97。SHAP算法确定血管内血栓切除术（EVT）尝试次数是影响CIR的关键因素。基于RF模型，可在https://ineffectivereperfusion.shinyapps.io/calculate/获取用于CIR预测的基于网络的计算器。最终模型包括十个参数：EVT尝试次数、糖尿病、既往缺血性中风、美国国立卫生研究院卒中量表（NIHSS评分）、术前基底节梗死、基线舒张压、计算机断层血管造影（CTA）上的血栓负荷评分（CBS）/基底动脉（BATMAN）评分、中风病因、侧支分级和MLS。
我们开发并验证了首个用于EVT后CIR预测的可解释机器学习模型，超越了传统方法。我们的CIR风险预测平台能够实现早期干预和个性化治疗。EVT尝试次数已成为关键决定因素，强调需要优化手术时机以改善预后。
