An epidemiologic model to project the impact of changes in glomerular filtration rate on quality of life and survival among persons with chronic kidney disease

Abstract

Purpose: Predicting the timing and number of end-stage renal disease (ESRD) cases from a population of individuals with pre-ESRD chronic kidney disease (CKD) has not previously been reported. The objective is to predict the timing and number of cases of ESRD occurring over the lifetime of a cohort of hypothetical CKD patients in the US based on a range of baseline estimated glomerular filtration rate (eGFR) values and varying rates of eGFR decline. Methods: A three-state Markov model - functioning kidney, ESRD, and death - with an annual cycle length is used to project changes in baseline eGFR on long-term health outcomes in a hypothetical cohort of CKD patients. Using published eGFR-specific risk equations and adjusting for predictive characteristics, the probability of ESRD (eGFR<10), time to death, and incremental cost-effectiveness ratios for hypothetical treatments (costing US$10, $5, and $2/day), are projected over the cohort's lifetime under two scenarios: an acute drop in eGFR (mimicking acute kidney injury) and a reduced hazard ratio for ESRD (mimicking an effective intervention). Results: Among CKD patients aged 50 years, an acute eGFR decrement from 45 mL/minute to 35 mL/minute yields decreases of 1.6 life-years, 1.5 quality-adjusted life-years (QALYs), 0.8 years until ESRD, and an increase of 183 per 1,000 progressing to ESRD. Among CKD patients aged 60 years, lowering the hazard ratio of ESRD to 0.8 yields values of 0.2, 0.2, 0.2, and 46 per 1,000, respectively. Incremental cost-effectiveness ratios are higher (ie, less favorable) for higher baseline eGFR, indicating that interventions occurring later in the course of disease are more likely to be economically attractive. Conclusion: Both acute kidney injury and slowing the rate of eGFR decline produce substantial shifts in expected numbers and timing of ESRD among CKD patients. This model is a useful tool for planning management of CKD patients. © 2014 Levy et al.