Background: Total kidney volume (TKV) is accepted by the Food and Drug Administration as a surrogate endpoint that is reasonably likely to predict clinical benefit in autosomal dominant polycystic kidney disease (ADPKD) and the most commonly used response biomarker for proof-of-concept intervention trials. However, the magnitude of treatment effect on TKV that would be predictive of a meaningful improvement in a clinical outcome, such as estimated glomerular filtration rate (eGFR), is unknown. Inference of this from observational studies has previously been approached by examining inter-individual variance in the relationship between TKV and GFR slopes over time. Methods: We developed a novel approach to modeling the intra-individual relationship between TKV and eGFR. Patients from the CRISP and HALT-A dataset were stratified by Mayo Imaging Class (MIC). Linear mixed models were fitted to eGFR with a fixed effect of log(TKV) and random intercepts, and the average slope within each MIC was estimated. Results: We found that within each MIC there is a consistent, linear relationship between log(TKV) and eGFR. The model predicts that within classes 1C–1E, for each 1% point per year reduction in TKV growth rate, the rate of eGFR decline would be reduced by 0.40 to 0.52 mL/min/1.73 m 2 per year. Conclusions: We have developed a new model that provides a framework for defining the magnitude of treatment effect on TKV that would support accelerated approval of a drug for ADPKD.
Asl et al. (Fri,) studied this question.