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

Abstract

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.

Incident and recurrent myocardial infarction (MI) in relation to comorbidities: Prediction of outcomes using machine‐learning algorithms

“…But this can be alleviated by advancing the increasing use of digital strategies aimed at enhancing the participation of patients and healthcare providers in integrated healthcare management. [13][14][15][16][17] Incorporation of such ML models into electronic health records may help the scalability of prevention strategies and improved management in terms of healthcare cost savings and better quality of care.…”

Systematic review and meta-analysis of prognostic models in Southeast Asian populations with acute myocardial infarction

“…The included studies assessed prognostic models in populations from Indonesia ( n = 1) ( 29 ), Malaysia ( n = 2) ( 30 , 31 ), Singapore ( n = 2) ( 32 , 33 ), and Thailand ( n = 2) ( 34 , 35 ). The total number of participants in the included studies ranged from 152 to 15,151 participants with a median of 4,701 participants.…”

Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study

“…This may be because the embedding representation contained a large number of diverse features extracted from a general EMR system, while many researchers selected AMI-related features with the assistance of clinical experts. For example, basic demographic data and few laboratory tests, as well as several specific features of AMI like Killip classification and left ventricular ejection fraction [ 19 , 30 ] were directly added into the machine learning model to predict mortality risk. Further, compared with other simple feature extraction methods like Principal Component Analysis [ 29 ] and the 3-layer autoencoder model, the proposed method took the association strength and feature importance into consideration, achieving higher predictive performance.…”