Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.

<h4>Background</h4>Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.<h4>Objective</h4>Apply machine learning for the prediction and identification of factors a...

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Autores principales:	Firdaus Aziz, Sorayya Malek, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff, Wan Azman Wan Ahmad, Rosli Mohd Ali, Kien Ting Liu, Gunavathy Selvaraj, Sazzli Kasim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a817c36b7d8421ca610c8f17b7f606a
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spelling	oai:doaj.org-article:7a817c36b7d8421ca610c8f17b7f606a2021-12-02T20:18:53ZShort- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.1932-620310.1371/journal.pone.0254894https://doaj.org/article/7a817c36b7d8421ca610c8f17b7f606a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254894https://doaj.org/toc/1932-6203<h4>Background</h4>Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.<h4>Objective</h4>Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.<h4>Methods</h4>The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.<h4>Results</h4>Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.<h4>Conclusions</h4>In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.Firdaus AzizSorayya MalekKhairul Shafiq IbrahimRaja Ezman Raja ShariffWan Azman Wan AhmadRosli Mohd AliKien Ting LiuGunavathy SelvarajSazzli KasimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0254894 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Firdaus Aziz Sorayya Malek Khairul Shafiq Ibrahim Raja Ezman Raja Shariff Wan Azman Wan Ahmad Rosli Mohd Ali Kien Ting Liu Gunavathy Selvaraj Sazzli Kasim Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
description	<h4>Background</h4>Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.<h4>Objective</h4>Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.<h4>Methods</h4>The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.<h4>Results</h4>Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.<h4>Conclusions</h4>In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.
format	article
author	Firdaus Aziz Sorayya Malek Khairul Shafiq Ibrahim Raja Ezman Raja Shariff Wan Azman Wan Ahmad Rosli Mohd Ali Kien Ting Liu Gunavathy Selvaraj Sazzli Kasim
author_facet	Firdaus Aziz Sorayya Malek Khairul Shafiq Ibrahim Raja Ezman Raja Shariff Wan Azman Wan Ahmad Rosli Mohd Ali Kien Ting Liu Gunavathy Selvaraj Sazzli Kasim
author_sort	Firdaus Aziz
title	Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
title_short	Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
title_full	Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
title_fullStr	Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
title_full_unstemmed	Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
title_sort	short- and long-term mortality prediction after an acute st-elevation myocardial infarction (stemi) in asians: a machine learning approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7a817c36b7d8421ca610c8f17b7f606a
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Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.

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