Comparison of the quality of logistic regression models and a classification tree in predicting hospital mortality in elderly patients with non-ST-elevation myocardial infarction

Abstract

Aim. Using the CHAID (Chi Squared Automatic Interaction Detection) method to develop a classification tree for predicting hospital mortality in patients with non-ST-elevation myocardial infarction (non-STEMI) aged 75 years and older and compare the quality of the constructed model with the logistic regression model.Material and methods. A retrospective analysis of the case histories of 119 sequentially enrolled patients aged ≥75 years who were hospitalized in a cardiology department due to non-STEMI in 2020-2021 was carried out. The construction of a predictive model of probability of dying was carried out using the logistic regression method. To assess the impact of various predictors affecting the probability of dying during the of hospitalization period in patients with non-STEMI, a classification tree was developed using the CHAID method. To compare the quality of logistic regression models and the classification tree, the areas under the ROC curve and confidence intervals were estimated.Results. Based on the construction of a binary logistic regression, it was found that the factors increasing hospital mortality were cardiogenic shock (CS): odds ratio (OR) 47.55; 4.00-589.16; p=0.002; new-onset atrial fibrillation: OR 6.45; 1.39-30.42; p=0.018; and the number of points on the GRACE scale: for each increase by 1 point: OR 1.03; 1,00-1,05; p=0.046. Similar data were obtained when analyzing the classification tree: in patients with CS, the predicted mortality was 91.7%. The probability of an unfavorable outcome based on the constructed classification tree was higher than the average in the analyzed sample in persons without CS, 2-3 degree atrioventricular blocks, and pulmonary edema, but with right bundle branch block on the electrocardiogram (25.0%) and in persons without CS and atrioventricular blockages of 2-3 degrees, but with pulmonary edema and a Q wave on the electrocardiogram (50.0%). Both methods of predicting hospital mortality are applicable. There were no statistically significant differences in the quality of both constructed models; the difference in the areas under the ROC curves was 0.043±0.268 with a 95% confidence interval of -0.055-0.141, p=0.387.Conclusion. Both developed methods can be used to determine the probability of dying in a hospital. Currently, the recruitment of patients into a prospective study of a similar design has begun and is continuing, during which validation of the constructed forecasting models is planned.