A High Granularity Machine Learning Model to Predict Successful Weaning During V‐V ECMO

ABSTRACT Purpose Weaning from venovenous extracorporeal membrane oxygenation (V‐V ECMO) remains unstandardized due to limited literature, with delayed weaning increasing the risk of vascular injury and multi‐organ failure. We aim to assess whether high‐granularity data, involving longitudinal ECMO, physiological, respiratory, and hemodynamic electronic medical record (EMR) variables, can predict V‐V ECMO weaning outcomes and outperform the Respiratory ECMO Survival Prediction (RESP) score. Methods We conducted a retrospective review of adult V‐V ECMO patients (≥ 18 years) at Johns Hopkins Hospital from 2016 to 2024. Machine learning (ML) models were developed using the RESP score alone (Model A) and EMR time‐series data across four intervals: pre‐ECMO, first 24 h, last 24 h before decannulation, and full ECMO run (Models B‐E). Random Forest, CatBoost, AdaBoost, and XGBoost algorithms were trained using 80/20 train‐test splits. Feature importance was assessed using Shapley Additive Explanations (SHAP) values. Results 119 V‐V ECMO patients (median age = 48%, 59.6% female) were included, with 62 (52.1%) experiencing mortality at discharge. Model E attained the highest performance with an area under the receiver operating characteristic curve (AUROC) of 0.904; Models A‐D achieved AUROCs of 0.815, 0.715, 0.515, and 0.881 respectively. SHAP analysis identified Body Mass Index (BMI), pulse, SpO 2 , and immunocompromised status as key features in predicting weaning success. BMI and pulse were strongest in the first 24 h, whereas age was most influential pre‐ECMO. Conclusion High‐granularity ML models accurately predicted V‐V ECMO weaning, with BMI, age, and immunocompromised status as key predictors. Their superior performance against RESP score‐based ML models suggests clinical utility for V‐V ECMO weaning.