A machine learning approach to model for end-stage liver disease score in cardiac surgery

Model for end-stage liver disease (MELD) likely has nonlinear effects on operative outcomes. We use machine learning to evaluate the nonlinear (dependent variable may not correlate one to one with an increased risk in the outcome) relationship between MELD and outcomes of cardiac surgery.

Society of Thoracic Surgery indexed elective cardiac operations between 2011 and 2018 were included. MELD was retrospectively calculated. Logistic regression models and an imbalanced random forest classifier were created on operative mortality. Cox regression models and random forest survival models evaluated survival. Variable importance analysis (VIMP) ranked variables by predictive power. Linear and machine-learned models were compared with receiver operator characteristic (ROC) and Brier score.

We included 3872 patients. Operative mortality was 1.7% and 5-year survival was 82.1%. MELD was the fourth largest positive predictor on VIMP analysis for operative long-term survival and the strongest negative predictor for operative mortality. MELD was not a significant predictor for operative mortality or long-term survival in the logistic or Cox regressions. The logistic model ROC area was 0.762, compared to the random forest classifier ROC of 0.674. The Brier score of the random forest survival model was larger than the Cox regression starting at 2 years and continuing throughout the study period. Bootstrap estimation on linear regression demonstrated machine-learned models were superior.

MELD and mortality are nonlinear. MELD was insignificant in the Cox multivariable regression but was strongly important in the random forest survival model and when using bootstrapping, the superior utility was demonstrated of the machine-learned models.