• Chao Liu, Qian Yuan, Zhi Mao, Pan Hu, Rilige Wu, Xiaoli Liu, Quan Hong, Kun Chi, Xiaodong Geng, and Xuefeng Sun.
• Medical School of Chinese PLA, 28 Fuxing Road, Beijing, China; 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, 28 Fuxing Road, Beijing, China.
• Am J Emerg Med. 2021 Aug 1; 46: 38-44.

IntroductionRhabdomyolysis (RM) is a complex set of clinical syndromes involving the rapid dissolution of skeletal muscles. The early detection of patients who need renal replacement therapy (RRT) is very important and may aid in delivering proper care and optimizing the use of limited resources.MethodsRetrospective analyses of the following three databases were performed: the eICU Collaborative Research Database (eICU-CRD), the Medical Information Mart for Intensive Care III (MIMIC-III) database and electronic medical records from the First Medical Centre of the Chinese People's Liberation Army General Hospital (PLAGH). The data from the eICU-CRD and MIMIC-III datasets were merged to form the derivation cohort. The data collected from the Chinese PLAGH were used for external validation. The factors predictive of the need for RRT were selected using a LASSO regression analysis. A logistic regression was selected as the algorithm. The model was built in Python using the ML library scikit-learn. The accuracy of the model was measured by the area under the receiver operating characteristic curve (AUC). R software was used for the LASSO regression analysis, nomogram, concordance index, calibration, and decision and clinical impact curves.ResultsIn total, 1259 patients with RM (614 patients from eICU-CRD, 324 patients from the MIMIC-III database and 321 patients from the Chinese PLAGH) were eligible for this analysis. The rate of RRT was 15.0% (92/614) in the eICU-CRD database, 17.6% (57/324) in the MIMIC-III database and 5.6% in the Chinese PLAGH (18/321). After the LASSO regression selection, eight variables were included in the RRT prediction model. The AUC of the model in the training dataset was 0.818 (95% CI 0.78-0.87), the AUC in the test dataset was 0.794 (95% CI 0.72-0.86), and the AUC in the Chinese PLAGH dataset (external validation dataset) was 0.820 (95% CI 0.70-0.86).ConclusionsWe developed and validated a model for the early prediction of the RRT requirement among patients with RM based on 8 variables commonly measured during the first 24 h after admission. Predicting the need for RRT could help ensure appropriate treatment and facilitate the optimization of the use of medical resources.Copyright © 2021 Elsevier Inc. All rights reserved.

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