Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach
Name
PAPER 1- ECHSA-MIT MACHINE LEARNING CHS.pdf
Description
Accepted version
Size
305.26 KB
Format
Adobe PDF
Checksum (MD5)
5b525dfb6f7103946a097be3504c1abd
Author(s)
Bertsimas, Dimitris
Zhuo, Daisy
Dunn, Jack
Levine, Jordan
Zuccarelli, Eugenio
Smyrnakis, Nikos
Tobota, Zdzislaw
Maruszewski, Bohdan
Fragata, Jose
Sarris, George E
Date Issued
2021
Journal
World Journal for Pediatric and Congenital Heart Surgery
Publisher
SAGE Publications
Citation
Bertsimas, Dimitris, Zhuo, Daisy, Dunn, Jack, Levine, Jordan, Zuccarelli, Eugenio et al. 2021. "Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach." World Journal for Pediatric and Congenital Heart Surgery, 12 (4).
Version
Author's final manuscript
Abstract
Objective: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS. Methods: We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. Results: Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors. Conclusions: The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1177/21501351211007106
