Developing in hospital mortality prediction model tools for patients with acute myocardial infarction (STEMI) using Yazd cardiovascular disease registry, YCDR data.

backgroundAn acute ST-elevation myocardial infarction (STEMI) is a medical event characterized by transmural myocardial ischemia that leads to myocardial injury or necrosis. This study was undertaken to develop, evaluate, and compare models for assessing the risk of hospital mortality in patients with acute myocardial infarction.

methodsThe study made use of data from the Yazd Cardiovascular Diseases Registry (YCDR), which is a cohort study of inpatient records of ischemic heart disease in Yazd province, Iran. A total of 1,861 patients who had experienced a STEMI were included in the analysis. Decision tree analysis was conducted using R software with the

resultsIn this study, three clinical, laboratory, and clinical-laboratory models were created. In a clinical-laboratory model, variables such as blood sugar (BS), triglycerides, HDL cholesterol, peak myocardial band (MBpick), CVA history, and low ejection fraction (EF) were found to increase the risk of in-hospital mortality in patients with ST-elevation myocardial infarction (STEMI). Conversely, higher levels of hemoglobin, low HDL-C, and previous myocardial infarction (MI) were associated with a protective effect against the risk of in-hospital mortality from acute myocardial infarction.The performance of the models in terms of Receiver Operating Characteristic (ROC) curve was 86.5%, 79.5%, and 90.2% for logistic regression model in three different models: clinical, laboratory, and combined clinical-laboratory. The accuracy of these models was calculated to be 88.3%, 81.3%, and 93%, respectively. Important variables influencing the prediction of in-hospital mortality in STEMI patients included Killip class, triglycerides, blood sugar, creatinine levels, the need for treatment due to ventricular fibrillation or ventricular tachycardia (VF/VT), age, and hemoglobin (HB). In the ROC curve analysis of the decision tree algorithm across the clinical, laboratory, and combined clinical-laboratory models, the performance levels were 74.6%, 69.8%, and 81.7%, respectively. The accuracy of the decision tree was 93.0%, 92.5%, and 95.8%.

conclusionThe findings of this study indicated that the decision tree algorithm had higher accuracy across all three models: clinical, laboratory, and combined clinical-laboratory compared to logistic regression. However, logistic regression showed higher sensitivity and better ROC curve performance than the decision tree algorithm.