Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?

Purpose We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers. Methods Data were derived from the electronic medical records of Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (estimated glomerular filtration rate [eGFR] < 60 mL.min(-1) -1 .73 m(-2) for >= 3 months) and followed up for at least 6 months. The study population was randomly divided into training and test sets. Results We identified 4,509 patients who met reasonable diagnostic criteria. Patients were randomly divided into 2 groups, and after excluding patients with missing data, the training and test sets included 1,625 and 1,618 patients, respectively. The integral mean was the most powerful explanatory (R-2 = 0.404) variable among the 8 modified values. Ten variables (age, sex, diabetes mellitus[DM], polycystic kidney disease[PKD], serum albumin, serum hemoglobin, serum phosphorus, serum potassium, eGFR (calculated by Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), and urinary protein) were included in the final risk prediction model for CKD stage 3 (R-2 = 0.330). Ten variables (age, sex, DM, GN, PKD, serum hemoglobin, serum blood urea nitrogen[BUN], serum calcium, eGFR(calculated by Modification of Diet in Renal Disease[MDRD]), and urinary protein) were included in the final risk prediction model for CKD stage 4 (R-2 = 0.386). Four variables (serum hemoglobin, serum BUN, eGFR(calculated by MDRD) and urinary protein) were included in the final risk prediction model for CKD stage 5 (R-2 = 0.321). Conclusion We created a prediction model according to CKD stages by using integral means. Based on the results of the Brier score (BS) and Harrel's C statistics, we consider that our model has significant explanatory power to predict the probability and interval time to the initiation of RRT.