Developing and Validating a New Model to Predict In-Hospital Mortality in Patients with Acute Myocardial Infarction
Journal of the College of Physicians and Surgeons Pakistan, cilt.33, sa.12, ss.1361-1366, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 33 Sayı: 12
- Basım Tarihi: 2023
- Doi Numarası: 10.29271/jcpsp.2023.12.1361
- Dergi Adı: Journal of the College of Physicians and Surgeons Pakistan
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, MEDLINE, Veterinary Science Database
- Sayfa Sayıları: ss.1361-1366
- Anahtar Kelimeler: Percutaneous coronary intervention, Mortality, In -hospital mortality, Prediction model, Logistic regression.
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
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 final 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 specificity was 96.2% (95% CI = 92.3-98.4). The AUC of the final 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 specificity 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 different populations.