• Martin Wagner, David Ansell, David M Kent, John L Griffith, David Naimark, Christoph Wanner, and Navdeep Tangri.
• Department of Medicine, Division of Nephrology, Tufts Medical Center, Boston, MA, USA. wagner_m@klinik.uni-wuerzburg.de
• Am. J. Kidney Dis. 2011 Jun 1;57(6):894-902.

BackgroundThe risk of death in dialysis patients is high, but varies significantly among patients. No prediction tool is used widely in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients using easily obtainable variables.Study DesignProspective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); models were developed using Cox proportional hazards.Setting & ParticipantsPatients initiating hemodialysis or peritoneal dialysis therapy in 2002-2004 who survived at least 3 months on dialysis treatment were followed up for 3 years. Analyses were restricted to participants for whom information for comorbid conditions and laboratory measurements were available (n = 5,447). The data set was divided into data sets for model development (n = 3,631; training) and validation (n = 1,816) using random selection.PredictorsBasic patient characteristics, comorbid conditions, and laboratory variables.OutcomesAll-cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up.ResultsIn the training data set, 1,078 patients (29.7%) died within the observation period. The final model for the training data set included patient characteristics (age, race, primary kidney disease, and treatment modality), comorbid conditions (diabetes, history of cardiovascular disease, and smoking), and laboratory variables (hemoglobin, serum albumin, creatinine, and calcium levels); reached a C statistic of 0.75 (95% CI, 0.73-0.77); and could discriminate accurately among patients with low (6%), intermediate (19%), high (33%), and very high (59%) mortality risk. The model was applied further to the validation data set and achieved a C statistic of 0.73 (95% CI, 0.71-0.76).LimitationsNumber of missing comorbidity data and lack of an external validation data set.ConclusionsBasic patient characteristics, comorbid conditions, and laboratory variables can predict 3-year mortality in incident dialysis patients with sufficient accuracy. Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients.Copyright © 2011 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

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