Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach
| Autor: | George E. Sarris, Bohdan Maruszewski, José Fragata, Zdzislaw Tobota, Eugenio Zuccarelli, Dimitris Bertsimas, Jack Dunn, Nikos Smyrnakis, Jordan Levine, Daisy Zhuo |
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| Rok vydání: | 2021 |
| Předmět: |
Heart Defects
Congenital medicine.medical_specialty business.industry Adverse outcomes Risk management tools General Medicine 030204 cardiovascular system & hematology Risk Assessment Surgery Machine Learning 03 medical and health sciences 0302 clinical medicine 030228 respiratory system Artificial Intelligence Risk Factors Pediatrics Perinatology and Child Health Medicine Humans Cardiology and Cardiovascular Medicine business |
| Zdroj: | World journal for pediatriccongenital heart surgery. 12(4) |
| ISSN: | 2150-136X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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