Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion

Abstract

ObjectivePrediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO. MethodsAll patients who underwent MT for ACLVO between 2015 – 2021 at a single institution were reviewed. Data was divided into 80% training and 20% test sets. 10 models were evaluated on the training set. The top 3 models underwent hyperparameter tuning using grid search with nested 5-fold CV to optimize the area under the receiver operating curve (AUROC). Tuned models were evaluated on the test set and compared to logistic regression. ResultsA total of 381 patients met the inclusion criteria. There were 50 (13.1%) patients who developed MMI. Out of the 10 ML models screened on the training set, the top 3 performing were neural network (median AUROC 0.78, IQR 0.72 – 0.83), support vector machine ([SVM] median AUROC 0.77, IQR 0.72 – 0.83), and random forest (median AUROC 0.75, IQR 0.68 – 0.81). On the test set, random forest (median AUROC 0.78, IQR 0.73 – 0.83) and neural network (median AUROC 0.78, IQR 0.73 – 0.83) were the top performing models, followed by SVM (median AUROC 0.77, IQR 0.70 – 0.83). These scores were significantly better than those for logistic regression (AUROC 0.72, IQR 0.66 – 0.78), individual risk factors, and the Malignant Brain Edema score (p < 0.001 for all). ConclusionML models predicted MMI with good discriminative ability. They outperformed standard statistical techniques and individual risk factors.