Table 1_Analysis of influencing factors and interaction effects on stroke recurrence in patients with middle cerebral artery occlusion treated with mechanical thrombectomy.docx

BackgroundStroke recurrence is an important factor affecting the prognosis of mechanical thrombectomy in patients with middle cerebral artery (MCA) occlusion. This study aims to construct a model for evaluating the degree of stroke recurrence and conduct binary and ternary interaction analysis.
MethodWe conducted a retrospective analysis of the clinical data of stroke recurrence patients, collecting demographic data, clinical characteristics, treatment factors, and biochemical indicators. Use XGBoost and RF models to screen features that contribute significantly to the degree of recurrence, and evaluate model performance through indicators such as ROC curve, F1 score, accuracy, and recall. Construct a stroke recurrence evaluation model based on the common features selected from these two models. Use the Andersson model to analyze the binary interaction between the model and other factors. Further analyze the three-way interaction between the model and other factors.
ResultBoth XGBoost and RF models perform well. In the multivariate logistic regression analysis, the recurrence model showed that age, smoking history, and infarct size had a significant impact on the degree of stroke recurrence (OR = 1.006, 1.214, 1.167, all p < 0.05), and the constructed recurrence model had a significant effect on the degree of stroke recurrence (OR = 1.346, p = 0.047). Through binary interaction analysis, it was found that there was a significant antagonistic effect between the recurrence model and age, smoking history, and infarct size. Triple interaction analysis showed that the synergistic effect of the recurrence model with age and smoking history was significant, and the synergistic effect of the recurrence model with smoking history and infarct size was also significant.
ConclusionAge, smoking history, and infarct size are important influencing factors on the degree of stroke recurrence in MCA occlusion patients after mechanical thrombectomy treatment. The recurrence model performs differently in different patient populations, and the interaction with age, smoking history, and infarct size is of great significance for evaluating the degree of stroke recurrence.

背景 卒中复发是影响大脑中动脉（MCA）闭塞患者机械取栓术预后的重要因素。本研究旨在构建卒中复发程度评估模型，并开展二元与三元交互作用分析。
方法 本研究对卒中复发患者的临床资料进行回顾性分析，收集其人口学资料、临床特征、治疗相关因素及生化指标。采用XGBoost与随机森林（RF）模型筛选对复发程度具有显著贡献的特征，并通过受试者工作特征（ROC）曲线、F1得分、准确率及召回率等指标评估模型性能。基于上述两种模型筛选得到的共同特征，构建卒中复发评估模型。采用Andersson模型分析该模型与其他因素间的二元交互作用，并进一步探究其与其他因素的三元交互作用。
结果 XGBoost与RF模型均表现优异。多因素logistic回归分析结果显示，年龄、吸烟史及梗死体积对卒中复发程度具有显著影响（比值比OR分别为1.006、1.214、1.167，均p<0.05）；所构建的复发模型对卒中复发程度亦存在显著影响（OR=1.346，p=0.047）。二元交互作用分析显示，该复发模型与年龄、吸烟史及梗死体积间存在显著拮抗作用。三元交互作用分析结果表明，复发模型与年龄、吸烟史的协同作用显著，复发模型与吸烟史、梗死体积的协同作用同样显著。
结论 年龄、吸烟史及梗死体积是影响大脑中动脉闭塞患者机械取栓术后卒中复发程度的重要危险因素。该复发模型在不同患者人群中的表现存在差异，其与年龄、吸烟史及梗死体积的交互作用对评估卒中复发程度具有重要意义。