Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used
Yong Li1,2
1No.2 Clinic, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
Artificial intelligence models were developed to predict in-hospital mortality in ST-segment elevation myocardial infarction (STEMI) patients. Logistic regression demonstrated the best predictive performance, identifying key risk factors for mortality.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Preventing in-hospital mortality in ST-segment elevation myocardial infarction (STEMI) is critical.
- Acute STEMI poses significant risks to patient survival during hospitalization.
Purpose of the Study:
- To develop and externally validate a diagnostic model for in-hospital mortality in acute STEMI patients.
- To leverage artificial intelligence methods for improved mortality prediction.
Main Methods:
- Utilized AI methods to develop and validate diagnostic models using training, testing, and validation datasets.
- Employed confusion matrix and Area Under the Receiver Operating Characteristic Curve (AUC) for model evaluation.
- Balanced unbalanced data to enhance model robustness.
Main Results:
- Identified key predictors of mortality: age, gender, cardiogenic shock, atrial fibrillation (AF), ventricular fibrillation (VF), third-degree atrioventricular block, bleeding, PCI, CABG, hypertension, diabetes, and prior MI.
- Logistic regression model achieved an F2 score of 0.81 (training), 0.6 (test), and 0.59 (validation).
- Logistic regression model achieved an AUC of 0.77 (training), 0.78 (test), and 0.8 (validation), indicating strong performance.
Conclusions:
- The diagnostic model built using logistic regression proved to be the most effective.
- The study successfully developed and validated AI-driven models for predicting in-hospital mortality in STEMI patients.
- This research provides a valuable tool for risk stratification and management of STEMI patients.
Background:
Preventing in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is a crucial step.
Objectives:
The objective of our research was to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients used artificial intelligence methods.
Methods:
We divided nonrandomly the American population with acute STEMI into a training set, a test set, and a validation set. We converted the unbalanced data into balanced data. We used artificial intelligence methods to develop and externally validate several diagnostic models. We used confusion matrix combined with the area under the receiver operating characteristic curve (AUC) to evaluate the pros and cons of the above models.
Results:
The strongest predictors of in-hospital mortality were age, gender, cardiogenic shock, atrial fibrillation (AF), ventricular fibrillation (VF), third degree atrioventricular block, in-hospital bleeding, underwent percutaneous coronary intervention (PCI) during hospitalization, underwent coronary artery bypass grafting (CABG) during hospitalization, hypertension history, diabetes history, and myocardial infarction history. The F2 score of logistic regression in the training set, the test set, and the validation dataset was 0.81, 0.6, and 0.59, respectively. The AUC of logistic regression in the training set, the test set, and the validation data set was 0.77, 0.78, and 0.8, respectively. The diagnostic model built by logistic regression was the best.
Conclusion:
The strongest predictors of in-hospital mortality were age, gender, cardiogenic shock, AF, VF, third degree atrioventricular block, in-hospital bleeding, underwent PCI during hospitalization, underwent CABG during hospitalization, hypertension history, diabetes history, and myocardial infarction history. We had used artificial intelligence methods developed and externally validated several diagnostic models of in-hospital mortality in acute STEMI patients. The diagnostic model built by logistic regression was the best. We registered this study with the registration number ChiCTR1900027129 (the WHO International Clinical Trials Registry Platform (ICTRP) on 1 November 2019).
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