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

• Published in: PloS one 2021-08, Vol.16 (8), p.e0254894
• 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
• Language: English
• Subjects: Acute coronary syndromes; Algorithms; Asian people; Bioinformatics; Biology and Life Sciences; Cardiology; Cardiovascular diseases; Cardiovascular research; Computer and Information Sciences; Datasets; Diuretics; Health risks; Heart attack; Heart attacks; Heart rate; Hospitals; Learning algorithms; Machine learning; Malaysia; Medicine and Health Sciences; Mortality; Myocardial infarction; Patients; Physical Sciences; Predictions; Prognosis; Research and Analysis Methods; Risk; Risk factors; Risk management; Statistics; Survival; Thrombolysis; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0254894

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 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.