Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Yixian Xu; Didi Han; Tao Huang; Xiaoshen Zhang; Hua Lu; Si Shen; Jun Lyu; Hao Wang

doi:10.3389/fcvm.2022.847206

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Front Cardiovasc Med. 2022 Feb 28:9:847206. doi: 10.3389/fcvm.2022.847206. eCollection 2022.

Authors

Yixian Xu¹, Didi Han², Tao Huang³, Xiaoshen Zhang⁴, Hua Lu⁴, Si Shen⁵, Jun Lyu³, Hao Wang¹

Affiliations

¹ Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
² School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
³ Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China.
⁴ Department of Cardiovascular Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China.
⁵ Department of Radiology, Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Abstract

Background: Rheumatic heart disease (RHD) accounts for a large proportion of Intensive Care Unit (ICU) deaths. Early prediction of RHD can help with timely and appropriate treatment to improve survival outcomes, and the XGBoost machine learning technology can be used to identify predictive factors; however, its use has been limited in the past. We compared the performance of logistic regression and XGBoost in predicting hospital mortality among patients with RHD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Methods: The patients with RHD in the MIMIC-IV database were divided into two groups retrospectively according to the availability of data and its clinical significance based on whether they survived or died. Backward stepwise regression was used to analyze the independent factors influencing patients with RHD, and to compare the differences between the two groups. The XGBoost algorithm and logistic regression were used to establish two prediction models, and the areas under the receiver operating characteristic curves (AUCs) and decision-curve analysis (DCA) were used to test and compare the models. Finally, DCA and the clinical impact curve (CIC) were used to validate the model.

Results: Data on 1,634 patients with RHD were analyzed, comprising 207 who died during hospitalization and 1,427 survived. According to estimated results for the two models using AUCs [0.838 (95% confidence interval = 0.786-0.891) and 0.815 (95% confidence interval = 0.765-0.865)] and DCA, the logistic regression model performed better. DCA and CIC verified that the logistic regression model had convincing predictive value.

Conclusions: We used logistic regression analysis to establish a more meaningful prediction model for the final outcome of patients with RHD. This model might be clinically useful for patients with RHD and help clinicians to provide detailed treatments and precise management.

Keywords: MIMIC-IV; XGBoost; intensive care unit; logistic regression; mortality; prediction; rheumatic heart disease.