• Zhongwang Li, Baoli Cheng, Jingya Wang, Guohao Xie, Xiaobo Yu, Man Huang, Zhijun Xu, Zhongqiu Lu, Huaqin Sun, Jian Zhang, Zhiyi Wang, Haiya Wu, Xu Liu, Lihua Chu, Jialian Zhao, and Xiangming Fang.
• Department of Anesthesiology and Intensive Care Unit, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China.
• J Crit Care. 2017 Dec 1; 42: 18-24.

PurposeThe objective of this study was to develop a model using a combination of routine clinical variables to predict mortality in critically ill patients.MethodsA cohort of 500 patients recruited from eight university hospital intensive care units (ICUs) was used to develop a model via logistic regression analyses. Discrimination and calibration analyses were performed to assess the model.ResultsThe model included the lactate level (odds ratio [OR]=1.11, 95% confidence interval [CI] 1.01 to 1.22, P=0.029), neutrophil-to-lymphocyte ratio (OR=1.03, 95% CI 1.01 to 1.04, P=0.002), acute physiology score (OR=1.11, 95% CI 1.06 to 1.15, P<0.001), Charlson comorbidity index (OR=1.36, 95% CI 1.15 to 1.60, P<0.001) and surgery type (OR: selective=Ref, no surgery=8.04, 95% CI 3.74 to 17.30, P<0.001, emergency=3.66, 95% CI 1.60 to 8.36, P=0.002). The model showed good discrimination (area under receiver operating characteristic curve: 0.84, 95% CI: 0.80 to 0.87) and calibration (Hosmer-Lemeshow test P=0.137) for predicting in-hospital mortality.ConclusionThe developed multifactor model can be used to effectively predict mortality in critically ill patients at ICU admission.Copyright © 2017 Elsevier Inc. All rights reserved.

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