Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

Abstract

Background: Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction in patients with heart disease, whereas their utility in long-term predictions is limited. This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality. Methods: This study used publicly available data from the Collaboration Center of Health Information Application at the Ministry of Health and Welfare, Taiwan, China. The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. We collected and analyzed mortality data up to December 2018. Medical records were used to gather demographic and clinical data, including age, gender, body mass index, percutaneous coronary intervention status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction, and non-ST-segment elevation myocardial infarction. Using the data, collected from 139 patients with AMI, from medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years. A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression (LR) as the baseline method. Results: The developed ML models achieved significantly better performance compared to the baseline LR (C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost, vs. 0.77 for LR) (PRF < 0.001, PAdaBoost < 0.001, and PXGBoost < 0.05). Adding bPEP and bET to our feature set significantly improved the performance of the algorithm, leading to an absolute increase in C-statistic of up to 0.03 (C-statistic, 0.83 for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs. 0.74 for LR) (PRF < 0.001, PAdaBoost < 0.001, PXGBoost < 0.05). Conclusion: The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases. This advancement may enable better treatment prioritization for high-risk individuals.

Keywords

• Electrocardiogram
• Predication
• Mortality prediction
• Machine learning