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In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine l...

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Main Authors:	Kasim, Sazzli, Malek, Sorayya, Song, Cheen, Ahmad, Wan Azman Wan, Fong, Alan, Ibrahim, Khairul Shafiq, Safiruz, Muhammad Shahreeza, Aziz, Firdaus, Hiew, Jia Hui, Ibrahim, Nurulain
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Published:	Public Library of Science 2022
Subjects:	RA Public aspects of medicine
Online Access:	http://eprints.um.edu.my/40246/
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spelling	my.um.eprints.402462023-10-17T07:02:01Z http://eprints.um.edu.my/40246/ In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm Kasim, Sazzli Malek, Sorayya Song, Cheen Ahmad, Wan Azman Wan Fong, Alan Ibrahim, Khairul Shafiq Safiruz, Muhammad Shahreeza Aziz, Firdaus Hiew, Jia Hui Ibrahim, Nurulain RA Public aspects of medicine Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes. Public Library of Science 2022-12 Article PeerReviewed Kasim, Sazzli and Malek, Sorayya and Song, Cheen and Ahmad, Wan Azman Wan and Fong, Alan and Ibrahim, Khairul Shafiq and Safiruz, Muhammad Shahreeza and Aziz, Firdaus and Hiew, Jia Hui and Ibrahim, Nurulain (2022) In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm. PLoS ONE, 17 (12). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0278944 <https://doi.org/10.1371/journal.pone.0278944>. 10.1371/journal.pone.0278944
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	RA Public aspects of medicine
spellingShingle	RA Public aspects of medicine Kasim, Sazzli Malek, Sorayya Song, Cheen Ahmad, Wan Azman Wan Fong, Alan Ibrahim, Khairul Shafiq Safiruz, Muhammad Shahreeza Aziz, Firdaus Hiew, Jia Hui Ibrahim, Nurulain In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm
description	Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.
format	Article
author	Kasim, Sazzli Malek, Sorayya Song, Cheen Ahmad, Wan Azman Wan Fong, Alan Ibrahim, Khairul Shafiq Safiruz, Muhammad Shahreeza Aziz, Firdaus Hiew, Jia Hui Ibrahim, Nurulain
author_facet	Kasim, Sazzli Malek, Sorayya Song, Cheen Ahmad, Wan Azman Wan Fong, Alan Ibrahim, Khairul Shafiq Safiruz, Muhammad Shahreeza Aziz, Firdaus Hiew, Jia Hui Ibrahim, Nurulain
author_sort	Kasim, Sazzli
title	In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm
title_short	In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm
title_full	In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm
title_fullStr	In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm
title_full_unstemmed	In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm
title_sort	in-hospital mortality risk stratification of asian acs patients with artificial intelligence algorithm
publisher	Public Library of Science
publishDate	2022
url	http://eprints.um.edu.my/40246/
_version_	1781704513461157888
score	13.211869

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

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