Ensemble machine learning prediction and variable importance analysis of 5-year mortality after cardiac valve and CABG operations

José Castela Forte*, Hubert E Mungroop, Fred de Geus, Maureen L van der Grinten, Hjalmar R Bouma, Ville Pettilä, Thomas W L Scheeren, Maarten W N Nijsten, Massimo A Mariani, Iwan C C van der Horst, Robert H Henning, Marco A Wiering, Anne H Epema

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Despite having a similar post-operative complication profile, cardiac valve operations are associated with a higher mortality rate compared to coronary artery bypass grafting (CABG) operations. For long-term mortality, few predictors are known. In this study, we applied an ensemble machine learning (ML) algorithm to 88 routinely collected peri-operative variables to predict 5-year mortality after different types of cardiac operations. The Super Learner algorithm was trained using prospectively collected peri-operative data from 8241 patients who underwent cardiac valve, CABG and combined operations. Model performance and calibration were determined for all models, and variable importance analysis was conducted for all peri-operative parameters. Results showed that the predictive accuracy was the highest for solitary mitral (0.846 [95% CI 0.812-0.880]) and solitary aortic (0.838 [0.813-0.864]) valve operations, confirming that ensemble ML using routine data collected perioperatively can predict 5-year mortality after cardiac operations with high accuracy. Additionally, post-operative urea was identified as a novel and strong predictor of mortality for several types of operation, having a seemingly additive effect to better known risk factors such as age and postoperative creatinine.

Original languageEnglish
Article number3467
Number of pages11
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusE-pub ahead of print - 10-Feb-2021

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