Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods.
Yong LiPublished in: Cardiology research and practice (2022)
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).
Keyphrases
- artificial intelligence
- st segment elevation myocardial infarction
- percutaneous coronary intervention
- machine learning
- big data
- deep learning
- atrial fibrillation
- clinical trial
- coronary artery bypass grafting
- end stage renal disease
- st elevation myocardial infarction
- acute myocardial infarction
- coronary artery disease
- newly diagnosed
- ejection fraction
- acute coronary syndrome
- blood pressure
- antiplatelet therapy
- chronic kidney disease
- type diabetes
- prognostic factors
- mental health
- heart failure
- cardiovascular disease
- high throughput
- peritoneal dialysis
- left ventricular
- liver failure
- emergency department
- respiratory failure
- intensive care unit
- patient reported outcomes
- drug induced
- adipose tissue
- randomized controlled trial
- aortic dissection
- catheter ablation
- glycemic control
- insulin resistance
- open label
- weight loss
- phase ii