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

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

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

Full description

Saved in:

Bibliographic Details
Main Authors:	Aziz, Firdaus, Malek, Sorayya, Ibrahim, Khairul Shafiq, Raja Shariff, Raja Ezman, Wan Ahmad, Wan Azman, Ali, Rosli Mohd, Liu, Kien Ting, Selvaraj, Gunavathy, Kasim, Sazzli
Format:	Article
Published:	Public Library of Science 2021
Subjects:	R Medicine (General)
Online Access:	http://eprints.um.edu.my/34148/
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.um.eprints.34148
record_format	eprints
spelling	my.um.eprints.341482022-09-01T03:53:56Z http://eprints.um.edu.my/34148/ Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach Aziz, Firdaus Malek, Sorayya Ibrahim, Khairul Shafiq Raja Shariff, Raja Ezman Wan Ahmad, Wan Azman Ali, Rosli Mohd Liu, Kien Ting Selvaraj, Gunavathy Kasim, Sazzli R Medicine (General) Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective 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. Methods 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. Results 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. Conclusions 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. Public Library of Science 2021 Article PeerReviewed Aziz, Firdaus and Malek, Sorayya and Ibrahim, Khairul Shafiq and Raja Shariff, Raja Ezman and Wan Ahmad, Wan Azman and Ali, Rosli Mohd and Liu, Kien Ting and Selvaraj, Gunavathy and Kasim, Sazzli (2021) Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach. PLoS ONE, 16 (8). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0254894 <https://doi.org/10.1371/journal.pone.0254894>. 10.1371/journal.pone.0254894
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine (General)
spellingShingle	R Medicine (General) Aziz, Firdaus Malek, Sorayya Ibrahim, Khairul Shafiq Raja Shariff, Raja Ezman Wan Ahmad, Wan Azman Ali, Rosli Mohd Liu, Kien Ting Selvaraj, Gunavathy Kasim, Sazzli Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach
description	Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective 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. Methods 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. Results 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. Conclusions 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	Aziz, Firdaus Malek, Sorayya Ibrahim, Khairul Shafiq Raja Shariff, Raja Ezman Wan Ahmad, Wan Azman Ali, Rosli Mohd Liu, Kien Ting Selvaraj, Gunavathy Kasim, Sazzli
author_facet	Aziz, Firdaus Malek, Sorayya Ibrahim, Khairul Shafiq Raja Shariff, Raja Ezman Wan Ahmad, Wan Azman Ali, Rosli Mohd Liu, Kien Ting Selvaraj, Gunavathy Kasim, Sazzli
author_sort	Aziz, Firdaus
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
publishDate	2021
url	http://eprints.um.edu.my/34148/
_version_	1744649159407828992
score	13.211869

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

Similar Items