Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with Rhabdomyolysis.

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Date: Sept. 1, 2021
Publisher: Lippincott Williams & Wilkins, WK Health
Document Type: Article
Length: 462 words

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Abstract :

Byline: CHAO LIU; XIAOLI LIU, School of Biological Science and Medical Engineering, Beihang University, Beijing, CHINA; ZHI MAO, Department of Critical Care Medicine, Chinese PLA General Hospital, Beijing, CHINA; PAN HU, Department of Anesthesiology, The 920 Hospital of Joint Logistic Support Force of Chinese PLA, Kunming Yunnan, CHINA; XIAOMING LI, Medical School of Chinese PLA, Beijing, CHINA; JIE HU, Department of Critical Care Medicine, Chinese PLA General Hospital, Beijing, CHINA; QUAN HONG; XIAODONG GENG; KUN CHI; FEIHU ZHOU, Department of Critical Care Medicine, Chinese PLA General Hospital, Beijing, CHINA; GUANGYAN CAI, Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, CHINA; XIANGMEI CHEN, Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, CHINA; XUEFENG SUN, Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, CHINA Abstract PURPOSE: Rhabdomyolysis (RM) is a complex set of clinical syndromes that involves the rapid dissolution of skeletal muscles. Mortality from RM is approximately 10%. This study aimed to develop an interpretable and generalizable model for early mortality prediction in RM patients. METHOD: Retrospective analyses were performed on two electronic medical record databases: the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III database. We extracted data from the first 24 h after patient ICU admission. Data from the two data sets were merged for further analysis. The merged data sets were randomly divided, with 70% used for training and 30% for validation. We used the machine learning model extreme gradient boosting (XGBoost) with the Shapley additive explanation method to conduct early and interpretable predictions of patient mortality. Five typical evaluation indexes were adopted to develop a generalizable model. RESULTS: In total, 938 patients with RM were eligible for this analysis. The area under the receiver operating characteristic curve (AUC) of the XGBoost model in predicting hospital mortality was 0.871, the sensitivity was 0.885, the specificity was 0.816, the accuracy was 0.915, and the F1 score was 0.624. The XGBoost model performance was superior to that of other models (logistic regression, AUC = 0.862; support vector machine, AUC = 0.843; random forest, AUC = 0.825; and naive Bayesian, AUC = 0.805) and clinical scores (Sequential Organ Failure Assessment, AUC = 0.747; Acute Physiology Score III, AUC = 0.721). CONCLUSIONS: Although the XGBoost model is still not great from an absolute performance perspective, it provides better predictive performance than other models for estimating the mortality of patients with RM based on patient characteristics in the first 24 h of admission to the ICU.

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Gale Document Number: GALE|A672227396