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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Autores principales:	Arman Kilic, Robert H. Habib, James K. Miller, David M. Shahian, Joseph A. Dearani, Artur W. Dubrawski
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	aortic valve replacement complications machine learning mortality risk prediction Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/3861eabb8530449181e695aa601676a0
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spelling	oai:doaj.org-article:3861eabb8530449181e695aa601676a02021-11-16T10:22:43ZSupplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction10.1161/JAHA.120.0196972047-9980https://doaj.org/article/3861eabb8530449181e695aa601676a02021-11-01T00:00:00Zhttps://www.ahajournals.org/doi/10.1161/JAHA.120.019697https://doaj.org/toc/2047-9980Background 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 P<0.001). Excluding deep sternal wound infection, the concordance indices ranged from 0.549 to 0.660 for discordant cases and 0.674 to 0.808 for concordant cases. Conclusions Supplementing ML models with existing STS models for surgical aortic valve replacement may have an important role in risk prediction and should be explored further. In particular, for the roughly 25% to 50% of patients demonstrating discordance in estimated risk between ML and STS, there appears to be a substantial decline in predictive performance suggesting vulnerability of the existing models in these patient subsets.Arman KilicRobert H. HabibJames K. MillerDavid M. ShahianJoseph A. DearaniArtur W. DubrawskiWileyarticleaortic valve replacementcomplicationsmachine learningmortalityrisk predictionDiseases of the circulatory (Cardiovascular) systemRC666-701ENJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 10, Iss 22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	aortic valve replacement complications machine learning mortality risk prediction Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	aortic valve replacement complications machine learning mortality risk prediction Diseases of the circulatory (Cardiovascular) system RC666-701 Arman Kilic Robert H. Habib James K. Miller David M. Shahian Joseph A. Dearani Artur W. Dubrawski Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction
description	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 P<0.001). Excluding deep sternal wound infection, the concordance indices ranged from 0.549 to 0.660 for discordant cases and 0.674 to 0.808 for concordant cases. Conclusions Supplementing ML models with existing STS models for surgical aortic valve replacement may have an important role in risk prediction and should be explored further. In particular, for the roughly 25% to 50% of patients demonstrating discordance in estimated risk between ML and STS, there appears to be a substantial decline in predictive performance suggesting vulnerability of the existing models in these patient subsets.
format	article
author	Arman Kilic Robert H. Habib James K. Miller David M. Shahian Joseph A. Dearani Artur W. Dubrawski
author_facet	Arman Kilic Robert H. Habib James K. Miller David M. Shahian Joseph A. Dearani Artur W. Dubrawski
author_sort	Arman Kilic
title	Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction
title_short	Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction
title_full	Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction
title_fullStr	Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction
title_full_unstemmed	Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction
title_sort	supplementing existing societal risk models for surgical aortic valve replacement with machine learning for improved prediction
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/3861eabb8530449181e695aa601676a0
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Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction

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