{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:26:40Z","timestamp":1769830000636,"version":"3.49.0"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Stroke 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>We 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Result<\/jats:title><jats:p>Both 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\u202f=\u202f1.006, 1.214, 1.167, all <jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.05), and the constructed recurrence model had a significant effect on the degree of stroke recurrence (OR\u202f=\u202f1.346, <jats:italic>p<\/jats:italic>\u202f=\u202f0.047). 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Age, 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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2025.1580950","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T14:42:37Z","timestamp":1755787357000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Analysis of influencing factors and interaction effects on stroke recurrence in patients with middle cerebral artery occlusion treated with mechanical 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