A New Risk Model Based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit

Abstract

Background and objectives : Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine learning risk prediction model for mortality in respiratory intensive care units (RICUs). Methods: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from January 1, 2012, to December 31, 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. Among the 1,063 visits, the RICU mortality rate was 13.5%. We constructed eXtreme Gradient Boosting (XGBoost) models and compared predictive performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. Results : For this dataset, XGBoost models achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.923 (95% CI: 0.889 – 0.957) in the test set, which was significantly greater than APACHE II (0.811, 95% CI: 0.778 – 0.844) and SOFA (0.805, 95% CI: 0.770 – 0.840). The Hosmer-Lemeshow statistic was 12.667 with a P-value of 0.124, which indicated a good calibration of our predictive model in the test set. The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NT-proBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; p<0.001). Conclusions : Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that NT-proBNP can be a good indicator of poor prognosis.