Extracorporeal Membrane Oxygenation (ECMO) is a life-saving cardiopulmonary support for patients with acute heart failure. However, the process of weaning from veno-arterial (V-A) ECMO remains complex and risky. We developed a machine learning-based predictive model to assist clinicians in identifying patients with a high probability of successful weaning. This retrospective monocentric study included 122 patients admitted to Rennes University Hospital between January 2020 and January 2023. Data from the eHOP clinical data warehouse were used to train and evaluate various machine learning algorithms, including Random Forest, XGBoost, KNN, SVM, and regularized logistic regressions. The best-performing models showed an AUC of 0.84–0.86, with XGBoost offering the highest results (0.86 [0.72–0.96]). Key predictors included ECMO flow rate, oxygenation fraction (FmO2), and duration of ECMO. While these results are promising, further validation is required before such tools can be translated into clinical decision-making processes.
