A Novel, Interpretable Machine Learning Model to Predict Neurological Outcomes Following Venoarterial Extracorporeal Membrane Oxygenation.

Abstract

Background: We used machine learning models incorporating rich electronic medical record (EMR) data to predict neurological outcomes after venoarterial extracorporeal membrane oxygenation (VA-ECMO).

Methods: This was a retrospective review of adult (≥ 18 years) patients undergoing VA-ECMO between 6/2016 and 4/2022 at a single center. The primary outcome was good neurological outcome, defined as a modified Rankin Scale score of 0 to 3, evaluated at hospital discharge. We extracted every measurement of 74 vital and laboratory values, as well as circuit and ventilator settings, from 24 h before cannulation through the entire duration of ECMO. An XGBoost model with Shapley Additive Explanations was developed and evaluated with leave-one-out cross-validation.

Results: Overall, 194 patients undergoing VA-ECMO (median age 58 years, 63% male) were included. We extracted more than 14 million individual data points from the EMR. Of 194 patients, 39 patients (20%) had good neurological outcomes. Three models were generated: model A, which contained only pre-ECMO data; model B, which added data from the first 48 h of ECMO; and model C, which included data from the entire ECMO run. The leave-one-out cross-validation area under the receiver operator characteristics curves for models A, B, and C were 0.72, 0.81, and 0.90, respectively. The inclusion of on-ECMO physiologic, laboratory, and circuit data greatly improved model performance. Both modifiable and nonmodifiable variables, such as lower body mass index, lower age, higher mean arterial pressure, and higher hemoglobin, were associated with good neurological outcome.

Conclusions: An interpretable machine learning model from EMR-extracted data was able to predict neurological outcomes for patients undergoing VA-ECMO with excellent accuracy.