Introduction: Mortality risk of patients with end-stage renal disease (ESRD) is highly elevated. Methods to estimate individual mortality risk are needed to provide individualized care and manage expanding ESRD populations. Many mortality prediction models exist but have shown deficiencies in model development (data comprehensiveness, validation) and in practicality. Therefore, our aim was to design 2 easy-to-apply prediction models for 1- and 2-year all-cause mortality in patients starting long-term renal replacement therapy (RRT).
Methods: We used data from the Finnish Registry for Kidney Diseases with complete national coverage of RRT patients. Model training group included all incident adult patients who started long-term dialysis in Finland in 2000 to 2008 (n = 4335). The external validation cohort consisted of those who entered dialysis in 2009 to 2012 (n = 1768). Logistic regression with stepwise variable selection was used for model building.
Results: We developed 2 prognostic models, both of which only included 6 to 7 variables (age at RRT start, ESRD diagnosis, albumin, phosphorus, C-reactive protein, heart failure, and peripheral vascular disease) and showed sufficient discrimination (c-statistic 0.77 and 0.74 for 1- and 2-year mortality, respectively). Due to a significantly lower mortality in the newer cohort, the models, to a degree, overestimated mortality risk.
Discussion: Mortality prediction algorithms could be more widely implemented into management of ESRD patients. The presented models are practical with only a limited number of variables and fairly good performance.
Keywords: algorithm; end-stage renal disease; mortality; prediction; registry; risk.