Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach

Background:: Several studies have highlighted the importance of considering sex differences in the diagnosis and treatment of Acute Coronary Syndrome (ACS). However, the identification of sex-specific risk markers in ACS sub-populations has been scarcely studied. The present study aims to explore ma...

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Autores principales:	Blanca Vázquez, Gibran Fuentes-Pineda, Fabian García, Gabriela Borrayo, Juan Prohías
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	In-hospital mortality prediction Machine learning Risk markers Acute Coronary Syndrome Sex differences Electronic health records Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/af950418c6ab4d5e8c5a3b097dc1eeb8
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spelling	oai:doaj.org-article:af950418c6ab4d5e8c5a3b097dc1eeb82021-12-04T04:35:21ZRisk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach2352-914810.1016/j.imu.2021.100791https://doaj.org/article/af950418c6ab4d5e8c5a3b097dc1eeb82021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352914821002616https://doaj.org/toc/2352-9148Background:: Several studies have highlighted the importance of considering sex differences in the diagnosis and treatment of Acute Coronary Syndrome (ACS). However, the identification of sex-specific risk markers in ACS sub-populations has been scarcely studied. The present study aims to explore machine learning (ML) models to identify in-hospital mortality markers for women and men in ACS sub-populations collected from a public database of electronic health records (EHR). Methods:: We extracted 1,299 patients with ST-elevation myocardial infarction (STEMI) and 2,820 patients with non-ST-elevation myocardial infarction (NSTEMI) from the Medical Information Mart for Intensive Care (MIMIC)-III database. We trained and validated mortality prediction models and used an interpretability technique to identify sex-specific markers for each sub-population. Results:: The models based on eXtreme Gradient Boosting (XGBoost) achieved the highest performance: area under the curve (AUC) = 0.94 (95% CI:0.84–0.96) for STEMI and AUC = 0.94 (95% CI:0.80–0.90) for NSTEMI. For STEMI, the top markers in women are chronic kidney failure, high heart rate, and age over 70 years. For men, the top markers are acute kidney failure, high troponin T levels, and age over 75 years. However, for NSTEMI, the top markers in women are low troponin levels, high urea levels, and age over 80 years. For men, the top markers are high heart rate, creatinine levels, and age over 70 years. Conclusions:: Our results show possible significant and coherent sex-specific risk markers of different ACS sub-populations by interpreting ML mortality models trained on EHRs. Differences are observed in the identified risk markers between women and men, highlighting the importance of considering sex-specific markers in implementing more appropriate treatment strategies and better clinical outcomes.Blanca VázquezGibran Fuentes-PinedaFabian GarcíaGabriela BorrayoJuan ProhíasElsevierarticleIn-hospital mortality predictionMachine learningRisk markersAcute Coronary SyndromeSex differencesElectronic health recordsComputer applications to medicine. Medical informaticsR858-859.7ENInformatics in Medicine Unlocked, Vol 27, Iss , Pp 100791- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	In-hospital mortality prediction Machine learning Risk markers Acute Coronary Syndrome Sex differences Electronic health records Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	In-hospital mortality prediction Machine learning Risk markers Acute Coronary Syndrome Sex differences Electronic health records Computer applications to medicine. Medical informatics R858-859.7 Blanca Vázquez Gibran Fuentes-Pineda Fabian García Gabriela Borrayo Juan Prohías Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach
description	Background:: Several studies have highlighted the importance of considering sex differences in the diagnosis and treatment of Acute Coronary Syndrome (ACS). However, the identification of sex-specific risk markers in ACS sub-populations has been scarcely studied. The present study aims to explore machine learning (ML) models to identify in-hospital mortality markers for women and men in ACS sub-populations collected from a public database of electronic health records (EHR). Methods:: We extracted 1,299 patients with ST-elevation myocardial infarction (STEMI) and 2,820 patients with non-ST-elevation myocardial infarction (NSTEMI) from the Medical Information Mart for Intensive Care (MIMIC)-III database. We trained and validated mortality prediction models and used an interpretability technique to identify sex-specific markers for each sub-population. Results:: The models based on eXtreme Gradient Boosting (XGBoost) achieved the highest performance: area under the curve (AUC) = 0.94 (95% CI:0.84–0.96) for STEMI and AUC = 0.94 (95% CI:0.80–0.90) for NSTEMI. For STEMI, the top markers in women are chronic kidney failure, high heart rate, and age over 70 years. For men, the top markers are acute kidney failure, high troponin T levels, and age over 75 years. However, for NSTEMI, the top markers in women are low troponin levels, high urea levels, and age over 80 years. For men, the top markers are high heart rate, creatinine levels, and age over 70 years. Conclusions:: Our results show possible significant and coherent sex-specific risk markers of different ACS sub-populations by interpreting ML mortality models trained on EHRs. Differences are observed in the identified risk markers between women and men, highlighting the importance of considering sex-specific markers in implementing more appropriate treatment strategies and better clinical outcomes.
format	article
author	Blanca Vázquez Gibran Fuentes-Pineda Fabian García Gabriela Borrayo Juan Prohías
author_facet	Blanca Vázquez Gibran Fuentes-Pineda Fabian García Gabriela Borrayo Juan Prohías
author_sort	Blanca Vázquez
title	Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach
title_short	Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach
title_full	Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach
title_fullStr	Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach
title_full_unstemmed	Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach
title_sort	risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: a machine learning approach
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/af950418c6ab4d5e8c5a3b097dc1eeb8
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Risk markers by sex for in-hospital mortality in patients with acute coronary syndrome: A machine learning approach

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