Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection

Abstract Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontan...

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Autores principales:	Chayakrit Krittanawong, Hafeez Ul Hassan Virk, Anirudh Kumar, Mehmet Aydar, Zhen Wang, Matthew P. Stewart, Jonathan L. Halperin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5e36399484af465693e619611655de7b
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spelling	oai:doaj.org-article:5e36399484af465693e619611655de7b2021-12-02T17:16:17ZMachine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection10.1038/s41598-021-88172-02045-2322https://doaj.org/article/5e36399484af465693e619611655de7b2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88172-0https://doaj.org/toc/2045-2322Abstract Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL models using candidate clinical variables potentially associated with mortality. We partitioned the data into training and evaluation sets with cross-validation. We estimated model performance based on the area under the receiver-operator characteristics curve (AUC) and balanced accuracy. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We identified 375 SCAD patients of which mortality during the index hospitalization was 11.5%. The best-performing DL algorithm identified in-hospital mortality with AUC 0.98 (95% CI 0.97–0.99), compared to other ML models (P < 0.0001). For prediction of mortality using ML models in patients with SCAD, the AUC ranged from 0.50 with the random forest method (95% CI 0.41–0.58) to 0.95 with the AdaBoost model (95% CI 0.93–0.96), with intermediate performance using logistic regression, decision tree, support vector machine, K-nearest neighbors, and extreme gradient boosting methods. A deep neural network model was associated with higher predictive accuracy and discriminative power than logistic regression or ML models for identification of patients with ACS due to SCAD prone to early mortality.Chayakrit KrittanawongHafeez Ul Hassan VirkAnirudh KumarMehmet AydarZhen WangMatthew P. StewartJonathan L. HalperinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chayakrit Krittanawong Hafeez Ul Hassan Virk Anirudh Kumar Mehmet Aydar Zhen Wang Matthew P. Stewart Jonathan L. Halperin Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
description	Abstract Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL models using candidate clinical variables potentially associated with mortality. We partitioned the data into training and evaluation sets with cross-validation. We estimated model performance based on the area under the receiver-operator characteristics curve (AUC) and balanced accuracy. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We identified 375 SCAD patients of which mortality during the index hospitalization was 11.5%. The best-performing DL algorithm identified in-hospital mortality with AUC 0.98 (95% CI 0.97–0.99), compared to other ML models (P < 0.0001). For prediction of mortality using ML models in patients with SCAD, the AUC ranged from 0.50 with the random forest method (95% CI 0.41–0.58) to 0.95 with the AdaBoost model (95% CI 0.93–0.96), with intermediate performance using logistic regression, decision tree, support vector machine, K-nearest neighbors, and extreme gradient boosting methods. A deep neural network model was associated with higher predictive accuracy and discriminative power than logistic regression or ML models for identification of patients with ACS due to SCAD prone to early mortality.
format	article
author	Chayakrit Krittanawong Hafeez Ul Hassan Virk Anirudh Kumar Mehmet Aydar Zhen Wang Matthew P. Stewart Jonathan L. Halperin
author_facet	Chayakrit Krittanawong Hafeez Ul Hassan Virk Anirudh Kumar Mehmet Aydar Zhen Wang Matthew P. Stewart Jonathan L. Halperin
author_sort	Chayakrit Krittanawong
title	Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
title_short	Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
title_full	Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
title_fullStr	Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
title_full_unstemmed	Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
title_sort	machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5e36399484af465693e619611655de7b
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Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection

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