• Haydn Hoffman, Sequeiros ChirinosJoelJDepartment of Neurology, The University of Tennessee Health Sciences Center, Memphis, Tennessee, USA., Nickalus Khan, Christopher Nickele, Violiza Inoa, Lucas Elijovich, Cheran Elangovan, Balaji Krishnaiah, Daniel Hoit, Adam S Arthur, and Nitin Goyal.
• Semmes Murphey Clinic, Memphis, Tennessee, USA. Electronic address: hhoffman@semmes-murphey.com.
• World Neurosurg. 2024 Dec 6: 123455123455.

BackgroundSymptomatic 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.MethodsConsecutive 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.ResultsA 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.ConclusionsAn ML model accurately predicted sICH prior to MT. It performed better than a standard statistical model and previously described clinical prediction models.Copyright © 2024. Published by Elsevier Inc.

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