Applying machine learning methods to predict operative mortality after tricuspid valve surgery

Abstract
Background
EuroSCORE stratifies surgical risk in cardiac surgery; however, it is not explicitly for tricuspid valve surgery. Therefore, we aimed to apply machine learning (ML) methods to predict operative mortality after tricuspid valve surgery and compare the predictive ability of these models to EuroSCORE.
This retrospective analysis included 1161 consecutive patients who underwent tricuspid valve surgery at a single center from 2009 to 2021. The study outcome was operative mortality (n=112), defined as mortality occurring within 30 days of surgery or the same hospital admission. Random forest, LASSO, elastic net, and logistic regression were used to identify predictors of operative mortality.

Results
EuroSCORE was significantly higher in patients who had operative mortality [8.52 (4.745–20.035) vs.4.11 (2.29–6.995), P<0.001] [AUC=0.73]. Random forest identified eight variables predicting operative mortality with an accuracy of 92% in the test set (age≥70 years, heart failure, emergency surgery, chronic kidney disease grade IV, diabetes mellitus, tricuspid valve replacement, hypertension, and redo surgery). The classification error rate in the training data was 9%, and in the testing data, it was 4.8%. Logistic regression identified eight variables with an AUC of 0.76. LASSO identified 13 variables with an AUC of 0.78, and elastic net identified 17 variables (AUC=0.795). The AUCs of the elastic net (P=0.048) and random forest (P<0.001) models were significantly higher than that of EuroSCORE.

Conclusions
ML effectively predicted TV surgery mortality more accurately than the traditional risk-scoring method. Incorporating ML in cardiac surgery risk scoring with comprehensive inclusion of all possible variables is recommended.