Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A
Firdaus Aziz1, Sorayya Malek1, Khairul Shafiq Ibrahim2,3,4
1Bioinformatics Division, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia.
Insights
Machine learning models significantly improve mortality prediction in Asian ST-segment elevation myocardial infarction (STEMI) patients compared to the Thrombolysis in Myocardial Infarction (TIMI) score. This approach identifies key factors for better risk stratification and patient outcomes.
Area of Science:
- Cardiology
- Data Science
- Biomedical Informatics
Background:
- Conventional risk scores for ST-segment elevation myocardial infarction (STEMI) mortality lack population specificity.
- Predicting short- and long-term mortality in diverse Asian populations requires tailored approaches.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting mortality in Asian STEMI patients.
- To identify key factors associated with mortality in this demographic.
- To compare ML model performance against the conventional Thrombolysis in Myocardial Infarction (TIMI) score.
Main Methods:
- Utilized the National Cardiovascular Disease Database for Malaysia registry data from a multi-ethnic Asian population.
- Developed in-hospital, 30-day, and 1-year mortality prediction models using 50 variables.
- Employed feature selection and ML algorithms, comparing outcomes with the TIMI score.
Main Results:
- ML models achieved superior AUC values (0.73-0.90) compared to the TIMI score (AUC=0.76-0.81) across all time points.
- ML algorithms identified age, heart rate, Killip class, glucose, and revascularization strategies as critical predictors.
- ML models classified 90% of non-survivors as high risk, significantly outperforming TIMI's 10-30% classification.
Conclusions:
- Machine learning offers superior mortality prediction for STEMI patients in multi-ethnic Asian populations compared to the TIMI score.
- ML facilitates the identification of population-specific risk factors, enabling personalized risk stratification.
- Continuous validation of ML models holds potential for improved patient management and outcomes.
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.
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