Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV)

Abstract

Background

Heart failure (HF) and atrial fibrillation (AF) usually coexist and are associated with a poorer prognosis. This study aimed to develop a model to predict in-hospital mortality in patients with HF combined with AF.

Methods

Patients with HF and AF were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database from 2008 to 2019. Feature selection was based on the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model. Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN) models, and their stacked model (the stacking ensemble model) were established. The area under of the curve (AUC) with 95% confidence interval (CI), sensitivity, specificity, as well as accuracy were applied to assess the performance of the predictive models.

Results

A total of 5,998 patients with HF combined with AF were included, of which 4,198 patients were assigned to the training set and 1,800 to the testing set (7:3). Among these 4,198 patients, 624 (14.86%) died in-hospital and 3,574 (85.14%) survived. Twenty-two features were used to construct the predictive model. Among these four single models, the AUC was 0.747 (95%CI: 0.717–0.777) for the Random Forest model, 0.755 (95%CI: 0.725–0.785) for the XGBoost model, 0.754 (95%CI: 0.724–0.784) for the LGBM model, and 0.746 (95%CI: 0.716–0.776) for the KNN model in the testing set. The stacking ensemble model had the highest AUC compared to the four single models, with AUCs of 0.837 (95%CI: 0.821–0.852) and 0.768 (95%CI: 0.740–0.796) for the training set and testing set, respectively.

Conclusion

The stacking ensemble model showed a good predictive effect in predicting in-hospital mortality in patients with HF combined with AF and may provide clinicians with a reference tool for early identification of mortality risk.