A multifactor model for predicting mortality in critically ill patients: A multicenter prospective cohort study

Abstract Purpose The objective of this study was to develop a model using a combination of routine clinical variables to predict mortality in critically ill patients. Methods A 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. Results The 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 P P 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. Conclusion The developed multifactor model can be used to effectively predict mortality in critically ill patients at ICU admission.