![]() ![]() Studies in the modern era have provided some guidance in treating this subset of patients: the provision of early, more potent therapies has been shown to improve outcomes in patients at a particularly elevated risk of adverse outcomes. Patients with NSTE-ACS represent a heterogeneous population with a wide variation in short-term and long-term clinical outcomes, which makes a uniform, standardised treatment approach ineffective and inappropriate. NSTE-ACS accounts for the bulk of acute coronary syndrome presentations in the UK, but management strategies in this group of patients have remained a subject of debate for decades. We summarise the international guidelines surrounding risk stratification as well as discuss new emerging data for future development of a new risk model in the management of patients with non-ST segment elevation acute coronary syndrome (NSTE-ACS). Continuous testing and validation will improve future risk classification, management, and results. ML improves death prediction by identifying separate characteristics in older Asian populations. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. 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. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). The TIMI score underestimates mortality in the elderly. DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). The main performance metric was the area under the receiver operating characteristic curve (AUC). The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. 50 variables helped in establishing the in-hospital death prediction model. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 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. ![]() 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). The SIRI could increase the prognostic value of the GRACE risk score.Higher SIRI is associated with a more severe disease status.The SIRI is a strong and independent risk factor for adverse outcomes in patients with ACS undergoing percutaneous coronary intervention.SIRI was an independent risk factor for MACE and provided incremental prognostic information in patients with ACS undergoing percutaneous coronary intervention. The restricted cubic spline showed a monotonic increase with a greater SIRI value for MACE (p <. 040 net reclassification improvement: 0.175, p =. The addition of SIRI had an incremental effect on the predictive ability of the Global Registry of Acute Coronary Events risk score for MACE (integrated discrimination improvement: 0.007, p =. The results were consistent in multiple sensitivity analyses. Multivariate Cox analysis showed that SIRI was significantly associated with MACE (hazard ratio: 1.127, 95% confidence interval: 1.034–1.229 p =. The primary endpoint was the composite of major adverse cardiovascular events (MACE), including overall death, non-fatal myocardial infarction, non-fatal stroke, and unplanned repeat revascularization.ĭuring a median follow-up of 927 days, 355 patients had MACE. The aim of this study was to examine the association between SIRI and adverse events in patients with the acute coronary syndrome (ACS) undergoing percutaneous coronary intervention.Ī total of 1724 patients with ACS enrolled from June 2016 to November 2017 at a single centre were included in this study, and SIRI was calculated for each patient. The systemic inflammatory response index (SIRI) is a novel inflammatory biomarker in many diseases. ![]()
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