Abstract
BACKGROUND: The 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 DESIGN: Prospective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); models were developed using Cox proportional hazards. SETTING & PARTICIPANTS: Patients 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. PREDICTORS: Basic patient characteristics, comorbid conditions, and laboratory variables. OUTCOMES: All-cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up. RESULTS: In 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). LIMITATIONS: Number of missing comorbidity data and lack of an external validation data set. CONCLUSIONS: Basic 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.