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Developing and Validating a New Model to Predict In-Hospital Mortality in Patients with Acute Myocardial Infarction
Selma Atay Islam1, Mehmet Muzaffer Islam2, Hande Akbal Kahraman1
1Department of Emergency Medicine, University of Health Sciences, Sancaktepe Training and Research Hospital, Sancaktepe, Turkey.
Insights
A new logistic regression model accurately predicts in-hospital mortality for patients undergoing percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI). This tool aids emergency medicine in identifying high-risk patients for better outcomes.
Area of Science:
- Cardiology
- Emergency Medicine
- Health Informatics
Background:
- Acute myocardial infarction (AMI) necessitates timely intervention, with percutaneous coronary intervention (PCI) being a primary treatment.
- Predicting in-hospital mortality in AMI patients undergoing PCI is crucial for resource allocation and patient management.
- Emergency departments (EDs) are the initial point of care for many AMI patients.
Purpose of the Study:
- To develop and validate a robust regression model for predicting in-hospital mortality in patients admitted to the ED with AMI and undergoing PCI.
- To identify key predictors of in-hospital mortality in this patient cohort.
Main Methods:
- A cohort study was conducted at a PCI-capable ED between January and March 2022.
- Backward stepwise logistic regression was employed to derive the prediction model.
- A non-random split-sample approach was used for internal and external validation.
- Predictors included ejection fraction, diastolic blood pressure, hemoglobin A1c, and hemoglobin.
Main Results:
- The final model demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.982 in the derivation cohort and 0.956 in the validation cohort.
- High sensitivity (92.3%) and specificity (96.2%) were observed in the derivation cohort.
- The validation cohort showed good performance with 80% sensitivity and 92.3% specificity.
Conclusions:
- The developed logistic regression model serves as an effective screening tool for predicting in-hospital mortality in STEMI/NSTEMI patients undergoing PCI in emergency settings.
- Further validation in diverse populations is recommended to enhance generalizability.
- The model can assist emergency medicine physicians in risk stratification and clinical decision-making.
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.
Key Words:
Percutaneous coronary intervention, Mortality, In-hospital mortality, Prediction model, Logistic regression.
