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.
DESIGN: A retrospective analysis of STEMI patients from 18 hospitals across Malaysia contributing to the Malaysian National Cardiovascular Database-acute coronary syndrome) registry (NCVD-ACS) year 2006-2013.
PARTICIPANTS: 16 517 patients diagnosed of STEMI from 18 hospitals in Malaysia from the year 2006 to 2013.
PRIMARY OUTCOME MEASURES: In-hospital and 30 day post-discharge mortality.
RESULTS: CS complicates 10.6% of all STEMIs in this study. They had unfavourable premorbid conditions and poor outcomes. The in-hospital mortality rate was 34.1% which translates into a 7.14 times mortality risk increment compared with STEMI without CS. Intravenous thrombolysis remained as the main urgent reperfusion modality. Percutaneous coronary interventions (PCI) in CS conferred a 40% risk reduction over non-invasive therapy but were only done in 33.6% of cases. Age over 65, diabetes mellitus, hypertension, chronic lung and kidney disease conferred higher risk of mortality.
CONCLUSION: Mortality rates of CS complicating STEMI in Malaysia are high. In-hospital PCI confers a 40% mortality risk reduction but the rate of PCI among our patients with CS complicating STEMI is still low. Efforts are being made to increase access to invasive therapy for these patients.