Prediction of Symptomatic Intracranial Hemorrhage Before Mechanical Thrombectomy Using Machine Learning in Patients with Anterior Circulation Large Vessel Occlusion

Background: 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. Methods: Consecutive adults who underwent MT for internal carotid artery/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 and previously described clinical prediction models. SHapley Additive exPlanations were used to identify the most predictive features in the ML model. Results: A total of 497 patients met inclusion criteria. The top performing ML model was extreme gradient boosting. The area under the receiver operating characteristics curve for the ML model on the test set was 0.79 (95% confidence interval [CI] 0.67–0.89), which was significantly higher (P < 0.001) than the logistic regression model (0.54 [95% CI 0.33–0.76]). The ML model also performed significantly better than the TAG = TICI-ASPECTS-glucose score (0.69 [95% CI 0.55–0.85], P < 0.001), Systolic blood pressure-Time-Blood glucose-ASPECTS 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 SHapley Additive exPlanations values the most predictive features of sICH in the ML model were lower Alberta Stroke Program Early CT score, lower collateral score, and higher presenting National Institutes of Health Stroke Scale. Conclusions: An ML model accurately predicted sICH prior to MT. It performed better than a standard statistical model and previously described clinical prediction models.