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AI-powered model for predicting mortality risk in VA-ECMO patients: a multicenter cohort study
Shuai Wang1, Sichen Tao2, Ying Zhu1
1Department of Critical Care, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, China.
Scientific Reports
|March 26, 2025
Summary
This study developed an AI model to predict 28-day mortality risk for patients weaning off veno-arterial extracorporeal membrane oxygenation (VA-ECMO). The eCMoML model shows high accuracy and generalizability, aiding clinical decisions for better patient outcomes.
Area of Science:
- Critical Care Medicine
- Artificial Intelligence in Healthcare
- Biomedical Engineering
Background:
- Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is vital for critically ill patients but carries high mortality risks.
- Improved prediction of mortality post-VA-ECMO weaning is crucial for clinical decision-making and patient management.
Purpose of the Study:
- To develop and validate a non-invasive, AI-enabled model for predicting 28-day mortality risk after VA-ECMO weaning.
- To identify key predictive features and assess the clinical utility of the developed model.
Main Methods:
- A multicenter retrospective cohort study involving 225 patients across five hospitals.
- Development and comparison of ten machine learning models using 25 selected patient features.
- Validation using internal and external cohorts, with performance evaluated by AUROC, SHAP, and decision curve analysis.
Main Results:
- The random forest model (eCMoML) demonstrated superior predictive performance with AUROC values of 1.00 (training), 1.00, 0.97, and 0.93 (validation cohorts).
- The model showed high accuracy, generalizability, and reliability despite limited training data.
- SHAP analysis identified important predictive features, and decision curve analysis confirmed clinical utility.
Conclusions:
- The eCMoML model provides a rapid, stable, and accurate tool for predicting 28-day mortality post-VA-ECMO weaning.
- This AI-driven approach can significantly enhance clinical decision-making, prognosis assessment, and treatment optimization.
- The model is expected to improve survival rates for patients undergoing VA-ECMO therapy.
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