Static and Dynamic Prediction of Chronic Renal Disease Progression Using Longitudinal Clinical Data from Taiwan’s National Prevention Programs

Kidney diseases can cause severe morbidity, mortality, and health burden. Determining the risk factors associated with kidney damage and deterioration has become a priority for the prevention and treatment of kidney disease. This study followed 1042 chronic kidney disease (CKD) patients with Stage 3–5 kidney disease who were treated at a public veteran’s hospital through the national prevention program. A total of 12.5 years of records of clinical measurements were collected and analyzed using dynamic and static Cox hazard models to predict the progression to dialysis treatment. The results showed that the statistical significance of several variables in patients with Stage 3–5 CKD was attenuated while the dynamic model was being used. The estimated glomerular filtration rate (eGFR) and urine protein to creatinine ratio (PCR) had the powerful ability to predict the progression of CKD patients with Stage 3a and Stage 3b–5 kidney disease, whereas serum calcium was also predictive for the progression of Stages 3b–5 CKD. Because these two sub-stages of Stage 3 CKD are often associated with differences in routine measurements and the risk analysis of renal dialysis, future research can use this predictive model as a reference while similar prevention programs are implemented.