Application of machine learning models in predicting prognosis after mechanical thrombectomy for acute ischemic stroke with large vessel occlusion in the anterior circulation

Objective Based on real⁃world clinical data, the predictive efficacy of the unweighted machine learning (ML) models for the prognosis of acute ischemic stroke with large vessel occlusion in the anterior circulation (AIS⁃aLVO) patients after mechanical thrombectomy was evaluated. The optimal model was selected, and the impact of class⁃weighted strategies on the predictive efficacy of this model was assessed. Methods A total of 191 patients with AIS⁃aLVO who underwent mechanical thrombectomy from May 2023 to September 2024 in Tianjin Huanhu Hospital were included. Collect their clinical data, such as pre⁃admission National Institutes of Health Stroke Scale (NIHSS) score, etc. Retrospectively analyze the brain non⁃contrast CT (NCCT), multi⁃phase CT angiography (mCTA) and CT perfusion (CTP) examinations of the patients upon admission. The mCTA was used to assess the collateral circulation status; the Alberta Stroke Program Early CT Score (ASPECTS) was used to evaluate the early ischemic changes in the middle cerebral artery (MCA) supply area based on the NCCT; the CTP was used to assess the cerebral perfusion status, and the Mismatch volume, Tmax > 4 s volume, Tmax > 6 s volume, Tmax > 8 s volume and Tmax > 10 s volume were obtained. The 90⁃day modified Rankin Scale (mRS) score after surgery was used as the prognostic evaluation index, and the score > 2 was determined as poor prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Logistic regression (LR), random forest (RF), support vector machine (SVM), decision tree (DT), k⁃nearest neighbor (KNN), and eXtreme Gradient Boosting (XGBoost) algorithms were used to construct unweighted models. The predictive efficacy of the models was evaluated using the receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve (Brier score), and decision curve analysis (DCA). The optimal model was selected, and the Shapley additive explanation (SHAP) method was used to analyze the feature importance of this model. At the same time, the impact of the class⁃weighted strategy on the predictive performance of the model was evaluated. Results The optimal regularization parameter (λ = 0.064) of LASSO regression was determined by the ten⁃fold cross⁃validation minimum deviation criterion. Four feature variables were selected: ASPECTS score, Tmax > 10 s volume, pre⁃admission NIHSS score, and poor collateral circulation status. Stratified sampling was used to randomly allocate the subjects to the training set (n = 133) and the test set (n = 58), and unweighted models was established. In the unweighted model, except for the overfitting RF and XGBoost models, the Delong test showed that the pairwise comparison of the AUC values of the remaining models had no statistical significance (P > 0.05, for all); however, the unweighted SVM model had the lowest Brier score (0.16), and its calibration ability was the strongest. Within the 15%-30% threshold range, the DCA curve of the unweighted SVM model was the highest, suggesting the highest clinical applicability. There was no statistically significant difference in the AUC values, sensitivity, specificity, accuracy, positive predictive value and negative predictive value between the class⁃weighted and unweighted SVM models (P > 0.05, for all); however, compared with the unweighted SVM model, the Brier score of the class⁃weighted SVM model was higher (0.17 vs. 0.16), and its calibration ability was weakened. Conclusions In a real⁃world cohort of AIS⁃aLVO cohort, the unweighted SVM model can accurately predict poor functional outcomes after mechanical thrombectomy without relying on class⁃weighted, and this method has high clinical translational potential.

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