Prediction Model of Inpatient Mortality for Patients with Myocardial Infarction

We propose and investigate a prediction model of inpatient mortality for patients with myocardial infarction. The model is based on complex clinical data from a hospital information system used in the Czech Republic. The prediction of the outcome is an important risk-adjustment factor for objective measurement of the quality of healthcare; thus it is a very important factor in healthcare quality assessment. For our experiments we studied hospital mortality in acute myocardial infarction, because: (1) this indicator is reliably detectable from available data; (2) treatment of acute myocardial infarction has a significant socio-economic impact; and (3) the prediction of mortality based on admission findings is the subject of many research papers and thus, we have a good benchmark for our experimental results. We considered only variables that convey information about the patient at the time of admission. We selected 21 out of 637 variables and used them as predictors in logistic regression to form a prediction model for hospital mortality. The achieved prediction accuracy was 85% and the size of the area under the ROC curve was 0.802. The results are based on a relatively small data sample of 486 patient records. Our future work will aim at increasing the accuracy by using a larger data set.