In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

Abstract

Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006–2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95–0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95–0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94–0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients’ risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient’s non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient’s non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

Artificial intelligence based prediction model of in-hospital mortality among females with acute coronary syndrome: for the Jerusalem Platelets Thrombosis and Intervention in Cardiology (JUPITER-12) Study Group

“…To the best of our knowledge, this is the first study to develop and train a machine-learning model for the prediction of in-hospital mortality exclusively for females with ACS. Prior studies have constructed models for the prediction of in-hospital mortality among all ACS patients ( 23 , 24 ).…”

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians

“…Previous research [ 19 , 49 , 51 ] has shown that features selected by SVM improve model performance when compared to other ML techniques. This led to the prioritization of SVM-ranked features in our feature selection procedure.…”

“…Despite its simplicity, NB can perform remarkably well, particularly when the independence assumption is obeyed. The selection of these algorithms was based on prior studies pertaining to mortality associated with cardiovascular disease [13,17,[48][49][50][51]. A combination of random search and manual adjustment was used to fine-tune the hyperparameters of the ML and stacked EL models, ensuring that the models are optimally configured for the task.…”

“…Although it demonstrated an AUC of 0.80 for 30-day mortality prediction in Western populations [70], its applicability in [71], while a study in China found AUCs of 0.52, 0.50, and 0.52 for 6-month, 1-year, and 2-year mortality risks [72]. According to Indonesian research, the 14-day mortality prediction AUC for NSTEMI was 0.61 and 0.63 for UA [73], while Malaysian data revealed an in-hospital mortality prediction AUC of only 0.55 [51]. The disparities in performance can be attributed to the TIMI score's origins in predominantly Caucasian populations, with a lack of representation of Asians, who frequently present with ACS at younger ages and have higher rates of diabetes, hypertension, renal failure, and delays in seeking medical care [74][75][76].…”

Machine Learning-Based Mortality Prediction of 90-Day Discharge in Acute Coronary Syndrome Patients

“…This predictive model utilizes data from the Korean Acute Myocardial Infarction Registry (KAMIR). Utilizing the National Cardiovascular Disease Database (NCVD) of Malaysia, models that combine DL and ML technologies have also been employed to stratify the in-hospital mortality risk of Asian ACS patients 14 , This further con rms the signi cant value of AI in enhancing the management e ciency of cardiovascular diseases.…”