Key result
The Random Forest-based eCMoML model accurately predicted 28-day mortality post-VA-ECMO weaning, achieving an AUROC of 1.00 in the training cohort and 0.93 to 1.00 across three validation cohorts.
Why the study?
VA-ECMO patients face high costs and significant mortality risks, prompting the development of an AI-enabled model to improve clinical decision-making.
Does the eCMoML machine learning model accurately predict 28-day mortality in adult patients successfully weaned from VA-ECMO?
Cohort (n=172)
Yes
Does the eCMoML machine learning model accurately predict 28-day mortality in adult patients successfully weaned from VA-ECMO?
Effect estimate: AUROC 1.00 in training cohort; 0.93 to 1.00 in validation cohorts
The eCMoML machine learning model provides a highly accurate, non-invasive tool for predicting 28-day mortality in patients weaned from VA-ECMO, potentially aiding clinical decision-making and prognosis assessment.
Should not yet guide post-VA-ECMO weaning decisions; leaves open prospective validation of ML models in critical care.
Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is a critical life support technology for severely ill patients. Despite its benefits, patients face high costs and significant mortality risks. To improve clinical decision-making, this study aims to develop a non-invasive, efficient artificial intelligence (AI)-enabled model to predict the risk of mortality within 28 days post-weaning from VA-ECMO. A multicenter, retrospective cohort study was conducted across five hospitals in China, including all the patients who received VA-ECMO support between January 2020 and January 2024. Based on the innovatively selected 25 easily obtainable patient examination features as potentially relevant, this study involved developing ten predictive models using both classical and advanced machine learning techniques. The model's performance is evaluated using various statistical metrics and the optimal predictive model are identified. Feature correlations are analyzed using Pearson correlation coefficients, and SHapley Additive exPlanations (SHAP) are employed to interpret feature importance. Decision curve analysis is used to evaluate the clinical utility of the predictive models. The study included 225 patients, with 66 patients from one hospital forming the training cohort. Three validation cohorts were used: internal validation with 16 patients from the training hospital and external validation with 30 and 60 patients from the other 4 hospitals. The random forest model emerged as the best predictor of 28-day mortality, achieving an AUROC of 1.00 in the training cohort and 1.00, 0.97, and 0.93 in the three validation cohorts, respectively. Despite the limited training data, the developed model, eCMoML, demonstrated high accuracy, generalizability and reliability. The model will be available online for immediate use by clinicians. The eCMoML model, validated in a multicenter cohort study, offers a rapid, stable, and accurate tool for predicting 28-day mortality post-VA-ECMO weaning. It has the potential to significantly enhance clinical decision-making, helping doctors better assess patient prognosis, optimize treatment plans, and improve survival rates.
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Wang et al. (2025) conducted a cohort in Cardiogenic shock requiring VA-ECMO (n=172). eCMoML (Random Forest predictive model) was evaluated on Prediction of 28-day mortality post-VA-ECMO weaning (AUROC) (AUROC 1.00 in training cohort; 0.93 to 1.00 in validation cohorts). The Random Forest-based eCMoML model accurately predicted 28-day mortality post-VA-ECMO weaning, achieving an AUROC of 1.00 in the training cohort and 0.93 to 1.00 across three validation cohorts.
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