ECMO PAL: Using Deep Neural Networks for Survival Prediction in Venoarterial Extracorporeal Membrane Oxygenation

Abstract

Abstract Purpose: Venoarterial extracorporeal membrane oxygenation (ECMO) is a complex and high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores are used clinically for patient prognostication and outcomes risk adjustment. This study aims to create the first artificial intelligence (AI)-driven ECMO survival score based on a large international patient cohort. Methods: A Deep Neural Network: ECMO Predictive Algorithm (ECMO PAL) was trained on 18,167 patients from the international Extracorporeal Life Support Organisation (ELSO) registry (2017 to 2020), and performance was measured using five-fold cross-validation. External validation was performed on all adult registry patients from 2021 (N = 5,015) and compared against existing prognostication scores: SAVE, Modified SAVE, and ECMO ACCEPTS for predicting in-hospital mortality. Results: Mean age was 56.8 ± 15.1 years, with 66.7% of patients being male and 50.2% having a pre-ECMO cardiac arrest. Cross-validation demonstrated an in-hospital mortality sensitivity and precision of 82.1 ± 0.2% and 77.6 ± 0.2%, respectively. Validation accuracy was only 2.8% lower than training accuracy, reducing from 75.5 to 72.7% [99% CI: 71.1 – 74.3%]. ECMO PAL accuracy outperformed the ECMO ACCEPTS (54.7%), SAVE (61.1%), and Modified SAVE (62.0%) scores. Conclusions: ECMO PAL is the first AI-powered ECMO survival score trained and validated on large international patient cohorts. ECMO PAL demonstrated high generalisability across ECMO regions and outperformed existing, widely used scores. Beyond ECMO, this study highlights how large international registry data can be leveraged for AI prognostication for complex critical care therapies.