Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning

Abstract

Venoarterial (VA) extracorporeal membrane oxygenation (ECMO) undoubtedly saves many lives, but it is associated with a high degree of patient morbidity, mortality, and resource use. This study aimed to develop a machine learning algorithm to augment clinical decision making related to VA-ECMO. Patients supported by VA-ECMO at a single institution from May 2011 to October 2018 were retrospectively reviewed. Laboratory values from only the initial 48 hours of VA-ECMO support were used. Data were split into 70% for training, 15% for validation, and 15% withheld for testing. Feature importance was estimated, and dimensionality reduction techniques were used. A deep neural network was trained to predict survival to discharge, and the final model was assessed using the independent testing cohort. Model performance was compared with that of the SAVE (Survival After Veno-arterial ECMO) score by using a receiver operator characteristic curve. Of the 282 eligible adult patients who were undergoing VA-ECMO, 117 (41%) survived to discharge. A total of 1.96 million laboratory values were extracted from the electronic medical record, from which 270 different summary variables were derived for each patient. The most important variables in predicting the primary outcome included lactate, age, total bilirubin, and creatinine. For the testing cohort, the final model achieved 82% overall accuracy and a greater area under the curve than the SAVE score (0.92 vs 0.65; P= .01) in predicting survival to discharge. This proof of concept study demonstrates the potential for machine learning models to augment clinical decision making for patients undergoing VA-ECMO. Further development with multi-institutional data is warranted.