In-hospital risk stratification algorithm of Asian elderly patients

Abstract

Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.

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

“…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 – 76 ]. Furthermore, the TIMI score has been criticized for underestimating mortality in high-risk groups [ 19 , 77 ], which is consistent with the current study’s findings.…”

“…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.…”

Risk-based Evaluation of ML Classification Methods Used for Medical Devices

“…In two cases, i.e. (30) and ( 34), the ML models were developed for mortality prediction. The concrete use of the ML models in clinical practice as well as the potential impact of errors was not addressed and not included in the evaluation, in these cases.…”

“…• Case AI: The three publications (30), (34), and (37) (out of a total of 30 publications), which had some kind of risk prediction, were considered as positive results. In this case, there was a 10% rate (3 out of 30) of publications including risk factors.…”

Machine learning prediction of mortality in Acute Myocardial Infarction

“…In recent years, research on ML in AMI has mainly focused on predicting patient mortality [ 18 , 23 – 28 ], prediction of patient readmission [ 29 ], or the occurrence of arrhythmia after acute myocardial infarction [ 25 ] and has already proved to be better predictors than the traditional statistical models [ 26 – 28 ]. Also, we found better performances of ML models than the traditional models, working on AMI mortality analysis in different settings and populations: Europe, the United States and Asia [ 18 , 23 – 28 ], mainly predicting one year or 30 days survival after AMI.…”