Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods

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 to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients used artificial intelligence methods. Methods We divide non-randomly the American population with acute STEMI into a training set, a test set,and a validation set. We converted the unbalanced data into balanc ed data. We used artificial intelligence methods to develop and externally validate the dia gnostic model of in-hospital mortality in acute STEMI patients. We used confusion matrix combined with the area under the receiver operating characteristic curve (AUC) to evaluat e 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 data set were 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 were 0.77, 0.78, and 0.8 respectively. The diagnostic model built by logistic regression was the best. Conclusion We had used artificial intelligence methods developed and externally validated the diagnostic model of in-hospital mortality in acute STEMI patients. We registered this study with WHO International Clinical Trials Registry Platform (ICTRP) (registration number: ChiCTR1900027129; registered date: 1 November 2019).