Development of a Machine Learning Model to Predict Outcomes and Cost after Cardiac Surgery

Rodrigo Zea-Vera, Christopher T. Ryan, Sergio M. Navarro, Jim Havelka, Matthew J. Wall, Joseph S. Coselli, Todd K. Rosengart, Subhasis Chatterjee, Ravi K. Ghanta

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Machine learning (ML) algorithms may enhance outcomes prediction and help guide clinical decision making. This study aimed to develop and validate a ML model that predicts postoperative outcomes and costs after cardiac surgery. Methods: The Society of Thoracic Surgeons registry data from 4874 patients who underwent cardiac surgery (56% coronary artery bypass grafting, 42% valve surgery, 19% aortic surgery) at our institution were divided into training (80%) and testing (20%) datasets. The Extreme Gradient Boosting decision-tree ML algorithms were trained to predict three outcomes: operative mortality, major morbidity or mortality, and Medicare outlier high hospitalization cost. Algorithm performance was determined using accuracy, F1 score, and area under the precision-recall curve (AUC-PR). The ML algorithms were validated in index surgery cases with The Society of Thoracic Surgeons risk scores for mortality and major morbidities and with logistic regression and were then applied to nonindex cases. Results: The ML algorithms with 25 input parameters predicted operative mortality (accuracy 95%; F1 0.31; AUC-PR 0.21), major morbidity or mortality (accuracy 71%, F1 0.47; AUC-PR 0.47), and high cost (accuracy 84%; F1 0.62; AUC-PR 0.65). Preoperative creatinine, complete blood count, patient height and weight, ventricular function, and liver dysfunction were important predictors for all outcomes. For patients undergoing nonindex cardiac operations, the ML model achieved an AUC-PR of 0.15 (95% CI, 0.05-0.32) for mortality and 0.59 (95% CI, 0.51-0.68) for major morbidity or mortality. Conclusions: The extreme gradient boosting ML algorithms can predict mortality, major morbidity, and high cost after cardiac surgery, including operations without established risk models. These ML algorithms may refine risk prediction after cardiac surgery for a wide range of procedures.

Original languageEnglish (US)
JournalThe Annals of thoracic surgery
DOIs
StatePublished - Jul 30 2022

Bibliographical note

Funding Information:
Christopher T. Ryan is supported by the NIH/NHLBI Research Training Program in Cardiovascular Surgery (T32 HL139430).

Funding Information:
Jeanie F. Woodruff, BS, ELS, of the Department of Scientific Publications at the Texas Heart Institute, provided editorial assistance. Christopher T. Ryan is supported by the NIH/NHLBI Research Training Program in Cardiovascular Surgery (T32 HL139430). Joseph S. Coselli serves as principal investigator, consults for, and receives royalties and a departmental educational grant from Terumo Aortic; consults and participates in clinical trials for Medtronic, Inc, W. L. Gore & Associates, and Abbott Laboratories; and participates in clinical trials for CytoSorbents and Edwards Lifesciences and CryoLife. Jim Havelka discloses a financial relationship with InformAI; Christopher T. Ryan with the National Institutes of Health.

Publisher Copyright:
© 2022 The Society of Thoracic Surgeons

PubMed: MeSH publication types

  • Journal Article

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