Applying an interpretive machine learning algorithm to predict in-hospital mortality in elderly asian patients with acute coronary syndrome (ACS)

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Mosti TED1 grant Background No study has used interpretative Machine Learning (ML) algorithms to predict in-hospital mortality for the Asian elderly (65+). TIMI predicts mortality for STEMI and NSTEMI using two different scores. It was based on the Western cohort with limited Asian data. Purpose Develop a single mortality risk scoring system for STEMI and NSTEMI patients and use interpretative ML to identify and analyse risk factors in elderly Asian patients with ACS. Methods The National Cardiovascular Disease Database registry identified 4305 elderly. 70% of the data was used to develop algorithms and 30% for validation. Fifty-four parameters were considered, including demographics, cardiovascular risk, medications, and clinical variables. A sequential backward elimination (SBE) algorithm was used to identify variables associated to elderly mortality. XGBoost classification algorithm and SHapley Additive exPlanation (SHAP) were used to understand mortality impact. The SHAP value of each variable represents its impact on model output (mortality). The main performance metric was area under the curve (AUC). The model was validated using a validation dataset and compared to STEMI and NSTEMI for TIMI. Results XGBoost's validation dataset performance using the top 12 predictors from SBE for; STEMI (AUC = 0.822, 95% CI: 0.775-0.868, Accuracy: 0.875, Sensitivity: 0.164, Specificity: 0.966) and NSTEMI (AUC = 0.853, 95% CI: 0.802-0.904, Accuracy: 0.950, Sensitivity: 0.154, Specificity: 0.997). XGBoost's validation dataset performance using the eight emergency predictors selected from the top twelve predictors for; STEMI (AUC = 0.813, 95% CI: 0.766-0.861, Accuracy: 0.868, Sensitivity: 0.194, Specificity 0.954) and NSTEMI (AUC = 0.867, 95% CI: 0.812-0.921, Accuracy: 0.941, Sensitivity: 0.333, Specificity: 0.978). Both models outperformed TIMI score (STEMI AUC = 0.702, NSTEMI AUC = 0.524). The predictors were chosen and ranked in ascending order using the SHAP values (Figure 1). On the y-axis, the variable names are displayed in ascending order of importance and the colour represents the feature's value, ranging from low to high, allowing comprehension of the distribution of SHAP values for each feature. The x-axis displays the SHAP values. Eight out of the twelve predictors were identified to be emergency variables and was ranked according to SHAP values (Figure 2). When compared to TIMI, cardiac catheterization, percutaneous coronary intervention, and pharmacotherapy drugs are chosen as predictors that improve mortality prediction in STEMI and NSTEMI elderly patients. High killip class and age are linked to a poorer ACS patient survival rate, but cardiac catheterization and use of pharmacotherapy drugs improve patient mortality. Conclusions A single algorithm can better classify elderly ASIAN patients with ACS than TIMI, which requires two scores. The use of interpretative algorithms aids in the understanding of ACS elderly hospital mortality factors.