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Developing and validating a new model to predict in-hospital mortality in patients with acute myocardial infarction

Author(s)
Selma Atay Islam Department of Emergency Medicine, University of Health Sciences, Sancaktepe Training and Research Hospital,Sancaktepe,Turkey
Mehmet Muza er Islam Department of Emergency Medicine, University of Health Sciences, Umraniye Training and Research Hospital,Umraniye,Turkey
Hande Akbal Kahraman Department of Emergency Medicine, University of Health Sciences, Sancaktepe Training and Research Hospital,Sancaktepe,Turkey
Muhammed Fatih Ciril Department of Emergency Medicine, University of Health Sciences, Sancaktepe Training and Research Hospital,Sancaktepe,Turkey
Aysegul Mizrak Department of Emergency Medicine, University of Health Sciences, Sancaktepe Training and Research Hospital,Sancaktepe,Turkey
Ismail Tayfur Department of Emergency Medicine, University of Health Sciences, Sancaktepe Training and Research Hospital,Sancaktepe,Turkey
Abstract
Objective: To derive and validate a regression model that can successfully and robustly predict in-hospital mortality of patients who underwent percutaneous coronary intervention (PCI) after admission to the Department of Emergency Medicine (ED) with acute myocardial infarction (AMI). Study Design: Cohort study. Place and Duration of the Study: ED of University of Health Sciences, Sancaktepe Training and Research Hospital, that worked as a PCI centre between January and March 2022. Methodology: Patients older than 18 years of age, diagnosed with acute ST elevation myocardial infarction (STEMI) or non-STEMI (NSTEMI) in the ED, and consequently underwent PCI were included. Patients with missing information of the outcome were excluded. For the regression model, backward stepwise logistic regression was utilised. The non-random split-sample development and validation method was used for the internal and external validation of the model. Ejection fraction, diastolic blood pressure, haemoglobin A1c, and haemoglobin were selected as the predictors. Results: A total of 279 patients were included in the analysis. The area under the curve (AUC) of the nal model in the derivation cohort was 0.982 (95% CI = 0.956-1.0). The sensitivity was 92.3% (95% CI = 64-99.8) and the speci city was 96.2% (95% CI = 92.3-98.4). The AUC of the nal model in the validation cohort was 0.956 (95% CI = 0.904-1.0). The sensitivity was 80% (95% CI = 28.3-99.5) and the speci city was 92.3% (95% CI = 84-97.1). Conclusion: The suggested model generated results that can be utilised as a screening tool for predicting in-hospital mortality in patients diagnosed with STEMI or NSTEMI who are admitted to PCI in emergency medicine settings. Nonetheless, it is essential to validate the model in di erent populations.
Publication Details
Page(s) 1361-1366
DOI 10.29271/jcpsp.2023.12.1361
Published Journal: Journal of College of Physicians and Surgeons--Pakistan : JCPSP, Volume: 33, Issue: 12, Year: 2023

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Keywords
Mortality, Percutaneous Coronary Intervention, Logistic Regression, In-hospital mortality, Prediction model
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