Prediction of symptomatic intracranial hemorrhage before mechanical thrombectomy using machine learning in patients with anterior circulation large vessel occlusion

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Symptomatic intracranial hemorrhage (sICH) after mechanical thrombectomy (MT) is associated with worse outcomes. We sought to develop and internally validate a machine learning (ML) model to predict sICH prior to MT in patients with anterior circulation large vessel occlusion. Consecutive adults who underwent MT for ICA/M1/M2 occlusions at a single institution were reviewed. The data was split into 80% training and 20% hold-out test sets. 9 ML models were screened. The top performing ML model was compared to logistic regression (LR) and previously described clinical prediction models. SHapley Additive exPlanations (SHAP) was used to identify the most predictive features in the ML model. A total of 497 patients met inclusion criteria. The top performing ML model was XGBoost. The area under the receiver operating characteristics curve (AUC) for the ML model on the test set was 0.79 (95% CI 0.67 - 0.89), which was significantly higher (p < 0.001) than the LR model (0.54 [95% CI 0.33 - 0.76]). The ML model also performed significantly better than the TAG score (0.69 [95% CI 0.55 - 0.85], p < 0.001), STBA score (0.45 [95% CI 0.30 - 0.60], p < 0.001), and ChatGPT 4.0 (0.60 [95% CI 0.48 - 0.68], p < 0.001). Based on SHAP values the most predictive features of sICH in the ML model were lower ASPECTS score, lower collateral score, and higher presenting NIHSS. A ML model accurately predicted sICH prior to MT. It performed better than a standard statistical model and previously described clinical prediction models.