Landsittel, Douglas P.

Key Points In autosomal dominant polycystic kidney disease, there is a linear relationship between the log-transformed total kidney volume and eGFR. There is a predictable change in the rate of eGFR decline with reduction in kidney growth rate. This framework can be used to determine treatment effect to support accelerated approval of drugs for polycystic kidney disease. Background Total kidney volume (TKV) is accepted by the US Food and Drug Administration as a surrogate end point that is reasonably likely to predict clinical benefit in autosomal dominant polycystic kidney disease 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 eGFR, is unknown. Inference of this from observational studies has previously been approached by examining interindividual variance in the relationship between TKV and GFR slopes over time. Methods We developed a novel approach to modeling the intraindividual relationship between TKV and eGFR. Patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease and Halt Progression of Polycystic Kidney Disease Study 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 0.40–0.52 ml/min per 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 autosomal dominant polycystic kidney disease. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2026_05_18_CJASNMay.21.5.51820.mp3

Key Points Data were collected from a cohort of 759 patients with autosomal dominant polycystic kidney disease that were followed for a median of 10.2 years. Prognostic models were developed to predict kidney failure using routinely obtained clinical information and externally validated. Risk predictions had good discrimination and calibration over a time horizon of 15 years. Background Autosomal dominant polycystic kidney disease (ADPKD) is a common cause of kidney failure. Progression is highly variable, and accurate prognostic information is needed to guide early treatment decisions. The objective of this study was to develop a multivariable predictive model for progression to kidney failure. Methods We developed prognostic models using Cox regression for the outcome of kidney failure, defined as eGFR <15 ml/min per 1.73 m 2 , dialysis, or kidney transplantation. The development dataset consisted of participants in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) and Halt Progression of Polycystic Kidney Disease study A (HALT-A) studies. The validation dataset consisted of patients with ADPKD in the Mayo clinical registry aged 15–49 years, with eGFR ≥60 ml/min per 1.73 m 2 . Predictor variables in the base model were age, sex, creatinine or eGFR, ADPKD genotype, and Mayo Imaging Class. Clinical and laboratory data were evaluated in the full model. Results The development cohort included 759 patients with baseline eGFR of 91±29 ml/min per 1.73 m 2 (mean±SD), of whom 16% reached end point after median (interquartile range) follow-up of 10.2 (5.5–16.7) years. The validation cohort included 535 patients with baseline eGFR of 90±31 ml/min per 1.73 m 2 (mean±SD), of whom 11% reached end point after median (interquartile range) follow-up of 5.5 (1.5–13.7) years. The full model, including age, sex, serum creatinine, Mayo Imaging Class, total carbon dioxide, hemoglobin, diastolic BP, and body mass index, had a C-index of 0.81 (95% confidence interval, 0.72 to 0.90) in the validation cohort and 0.75 (95% confidence interval, 0.62 to 0.88) when ADPKD genotype was also included. Risk predictions from base and full models were well-calibrated out to 15 years. Conclusions In persons with early ADPKD and preserved eGFR, a model using routinely obtained clinical information quantified risk of kidney failure over 15 years.