In-hospital risk stratification algorithm of Asian elderly patients

Kasim, Sazzli and Malek, Sorayya and Cheen, Song and Safiruz, Muhammad Shahreeza and Wan Ahmad, Wan Azman and Ibrahim, Khairul Shafiq and Aziz, Firdaus and Negishi, Kazuaki and Ibrahim, Nurulain (2022) In-hospital risk stratification algorithm of Asian elderly patients. Scientific Reports, 12 (1). ISSN 2045-2322, DOI

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Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.

Item Type: Article
Funders: Kementerian Sains, Teknologi dan Inovasi, Malaysia [TDF03211036]
Uncontrolled Keywords: Acute coronary syndrome; Elevation myocardial-infarction; Feature-selection; 30-day mortality; Global registry
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine
R Medicine > R Medicine (General)
Divisions: Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 04 Oct 2023 01:56
Last Modified: 04 Oct 2023 01:56

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