Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach

Background Studies had shown that mortality due to ST-elevation myocardial infarction (STEMI) is higher in women compared with men. The purpose of this study is to develop and validate prediction models for all-cause in-hospital mortality in women admitted with STEMI using logistic regression and random forest, and to compare the performance and validity of the different models. Methods Data from the National Inpatient Sample (NIS) data years 2011–2013 were used to identify women admitted with STEMI. The main outcome was all-cause in-hospital mortality. Patients were divided into development and validation cohorts, and trained models were internally validated using 20% of the 2012 data, and externally validated using 2011 and 2013 NIS data. Results Three main models were developed and compared; multivariate logistic regression, full and reduced random forest models. In the multivariate logistic regression, 11 variables were included in the final model based on backward elimination. The full random forest model contained 32 variables, and the reduced model contained 17 variables selected based on individual variable importance. In the internal validation cohort, the C-index was 0.84, 0.81, and 0.80 for the multivariate logistic regression, full, and reduced random forest models, respectively. The models showed good stability in the external validation cohorts with a C-index for the logistic regression, full, and reduced random forest models of 0.84, 0.85, and 0.81 for year 2011, and 0.82, 0.81, and 0.81 for year 2013, respectively. Conclusions Random forest was comparable to logistic regression in predicting in-hospital mortality in women with STEMI, and can be a useful and accurate tool in clinical practice.