ObjectiveWe aimed to determine if machine learning (ML) can predict acute brain injury (ABI) and identify modifiable risk factors for ABI in venoarterial extracorporeal membrane oxygenation (VA-ECMO) patients. MethodsWe included adults (≥18 years) receiving VA-ECMO or extracorporeal cardiopulmonary resuscitation (ECPR) in the Extracorporeal Life Support Organization Registry (2009-2021). Our primary outcome was ABI: central nervous system (CNS) ischemia, intracranial hemorrhage (ICH), brain death, and seizures. We utilized Random Forest, CatBoost, LightGBM and XGBoost ML algorithms (10-fold leave-one-out cross-validation) to predict and identify features most important for ABI. We extracted 65 total features: demographics, pre-ECMO/on-ECMO laboratory values, and pre-ECMO/on-ECMO settings. ResultsOf 35,855 VA-ECMO (non-ECPR) patients (median age=57.8 years, 66%=male), 7.7% (n=2,769) experienced ABI. In VA-ECMO (non-ECPR), the area under the receiver-operator characteristics curves (AUC-ROC) to predict ABI, CNS ischemia, and ICH was 0.67, 0.67, and 0.62, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively for ABI. Longer ECMO duration, higher 24h ECMO pump flow, and higher on-ECMO PaO2 were associated with ABI. Of 10,775 ECPR patients (median age=57.1 years, 68%=male), 16.5% (n=1,787) experienced ABI. The AUC-ROC for ABI, CNS ischemia, and ICH was 0.72, 0.73, and 0.69, respectively. Longer ECMO duration, older age, and higher 24h ECMO pump flow were associated with ABI. ConclusionsIn the largest study predicting neurological complications with ML in ECMO, longer ECMO duration and higher 24h pump flow were associated with ABI in non-ECPR and ECPR VA-ECMO.