Performance of the Models Predicting In-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infarction with Predictors in Categorical and Continuous Forms.

Unlabelled: is to assess the performance of predictive models developed on the basis of predictors in the continuous and categorical forms to predict the probability of in-hospital mortality (IHM) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI).

Materials And Methods: A single-center retrospective study has been conducted, within the framework of which data from 4674 medical records of patients with STEMI after PCI, treated at the Regional Vascular Center of Vladivostok (Russia), have been analyzed. Two groups of patients were identified: group 1 consisted of 318 (6.

Comparative Analysis of the Effectiveness of Riskometer Scales in Predicting the Risk of in-Hospital Mortality in Patients With ST-Segment Elevation Myocardial Infarction After Percutaneous Coronary Intervention.

Aim: Comparative evaluation of the effectiveness of riskometer scales in predicting in-hospital death (IHD) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) and the development of new models based on machine learning methods.

Material And Methods: A single-center cohort retrospective study was conducted using data from 4,675 electronic medical records of patients with STEMI (3,202 men and 1,473 women) with a median age of 63 years who underwent emergency PCI. Two groups of patients were isolated: group 1 included 318 (6.

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions.

Background And Objective: Despite the constant improvement of coronary heart disease (CHD) diagnostics and treatment methods it remains one of the main causes of death in most countries around the world. And myocardial infarction with ST segment elevation on the electrocardiogram (STEMI) still is one of the most dangerous clinical variants of CHD. This study aims to develop an explainable machine learning model for in-hospital mortality (IHM) risk prediction in STEMI patients after myocardial revascularization by percutaneous coronary intervention (PCI).