Early prediction of mortality upon intensive care unit admission - Yu-Chang Yeh; Yu-Ting Kuo; Kuang-Cheng Kuo; Yi-Wei Cheng; Ding-Shan Liu; Feipei Lai; Lu-Cheng Kuo; Tai-Ju Lee; Wing-Sum Chan; Ching-Tang Chiu; Ming-Tao Tsai; Anne Chao; et al. (BMC Medical Informatics and Decision Making)
Abstract Background We aimed to develop and validate models for predicting intensive care unit (ICU) mortality of critically ill adult patients as early as upon ICU admission. Methods Combined data of 79,657 admissions from two teaching hospitals’ ICU databases were used to train and validate the machine learning models to predict ICU mortality upon ICU admission and at 24 h after ICU admission by using logistic regression, gradient boosted trees (GBT), and deep learning algorithms. Results In the testing dataset for the admission models, the ICU mortality rate was 7%, and 38.4% of patients were discharged alive or dead within 1 day of ICU admission. The area under the receiver operating characteristic curve (0.856, 95% CI 0.845–0.867) and area under the precision-recall curve (0.331, 95% CI 0.323–0.339) were the highest for the admission GBT model. The ICU mortality rate was 17.4% in the 24-hour testing dataset, and the performance was the highest for the 24-hour GBT model. Conclusion The ADM models can provide crucial information on ICU mortality as early as upon ICU admission. 24 H models can be used to improve the prediction of ICU mortality for patients discharged more than 1 day after ICU admission.