Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 20...

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Bibliographic Details
Published in:Journal of the American Heart Association 2021-11, Vol.10 (22), p.e019697-e019697
Main Authors: Kilic, Arman, Habib, Robert H, Miller, James K, Shahian, David M, Dearani, Joseph A, Dubrawski, Artur W
Format: Article
Language:English
Subjects:
Adult
Aortic Valve - surgery
aortic valve replacement
Aortic Valve Stenosis - epidemiology
Aortic Valve Stenosis - surgery
complications
Heart Valve Prosthesis
Heart Valve Prosthesis Implantation - adverse effects
Humans
Machine Learning
mortality
Original Research
Risk Assessment
Risk Factors
risk prediction
Treatment Outcome
Wound Infection
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Summary:Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 2017 were included. ML models for operative mortality and major morbidity were previously developed using extreme gradient boosting. Concordance and discordance in predicted risk between ML and STS models were defined using equal-size tertile-based thresholds of risk. Calibration metrics and discriminatory capability were compared between concordant and discordant patients. A total of 243 142 patients were included. Nearly all calibration metrics were improved in cases of concordance. Similarly, concordance indices improved substantially in cases of concordance for all models with the exception of deep sternal wound infection. The greatest improvements in concordant versus discordant cases were in renal failure: ML model (concordance index, 0.660 [95% CI, 0.632-0.687] discordant versus 0.808 [95% CI, 0.794-0.822] concordant) and STS model (concordance index, 0.573 [95% CI, 0.549-0.576] discordant versus 0.797 [95% CI, 0.782-0.811] concordant) (each
ISSN:2047-9980
2047-9980
DOI:10.1161/JAHA.120.019697