A predictive model for progression of CKD

The prevalence of chronic kidney disease (CKD) in Taiwan is 11.9%, and the incidence and prevalence of end-stage renal disease (ESRD) is ranked first in the world. The severity of CKD progression to ESRD is dependent on glomerular filtration rate and proteinuria. However, the risk factors for ESRD also include diabetes, hypertension, hyperlipidemia, age, sex, and so on, and predicting CKD progression using few variables is insufficient. Currently, there are no models with high accuracy and high explanatory power that could predict the risk of progression to dialysis in CKD patients in Taiwan. Our aim was to establish an optimal prediction model for CKD progression in patients This study was a retrospective cohort study, which reviewed data from the “Public health insurance Pre-ESRD preventive program and patient health education program” that was implemented by the National Health Insurance Administration, Ministry of Health and Welfare. From 2006 to 2013, data of CKD patients from the Tri-Service General Hospital in Neihu District, Taipei City was examined. The data collected in this study included demographic variables, past medical history, and blood biochemical values. After exclusion of variables with >30% missing data, the remaining variables were interpolated using multiple imputations and inputted into the prediction model for analysis. The Cox proportion hazard model was used to investigate the influence of CKD risk factors on progression to dialysis. The strengths of various models were evaluated using likelihood ratios (LR), in order to identify a model which uses the least factors but has the strongest explanatory power. The study results included 1549 CKD patients, of whom 1017 eventually had dialysis. This study found that in the prediction model with the best explanatory power, the influencing factors and hazard ratios (HR) were: age 0.95 (0.91–0.99), creatinine 1.03 (1.02–1.05), urea nitrogen 1.18 (1.14–1.23), and comorbid systemic diabetes 1.65 (1.45–1.88). A prediction model was developed in this study, which could be used to carry out predictions based on blood biochemical values from patients, in order to accurately predict the risk of CKD progression to dialysis.