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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.

Background and objectives The progression of polycystic liver disease is not well understood. The purpose of the study is to evaluate the associations of polycystic liver progression with other disease progression variables and classify liver progression on the basis of patient's age, height-adjusted liver cystic volume, and height-adjusted liver volume. Design, setting, participants, & measurements Prospective longitudinal magnetic resonance images from 670 patients with early autosomal dominant polycystic kidney disease for up to 14 years of follow-up were evaluated to measure height-adjusted liver cystic volume and height-adjusted liver volume. Among them, 245 patients with liver cyst volume >50 ml at baseline were included in the longitudinal analysis. Linear mixed models on log-transformed height-adjusted liver cystic volume and height-adjusted liver volume were fitted to approximate mean annual rate of change for each outcome. The association of sex, body mass index, genotype, baseline height-adjusted total kidney volume, and Mayo imaging class was assessed. We calculated height-adjusted liver cystic volume ranges for each specific age and divided them into five classes on the basis of annual percentage increase in height-adjusted liver cystic volume. Results The mean annual growth rate of height-adjusted liver cystic volume was 12% (95% confidence interval, 11.1% to 13.1%; P <0.001), whereas that for height-adjusted liver volume was 2% (95% confidence interval, 1.9% to 2.6%; P <0.001). Women had higher baseline height-adjusted liver cystic volume than men, but men had higher height-adjusted liver cystic volume growth rate than women by 2% (95% confidence interval, 0.4% to 4.5%; P =0.02). Whereas the height-adjusted liver cystic volume growth rate decreased in women after menopause, no decrease was observed in men at any age. Body mass index, genotype, and baseline height-adjusted total kidney volume were not associated with the growth rate of height-adjusted liver cystic volume or height-adjusted liver volume. According to the height-adjusted liver cystic volume growth rate, patients were classified into five classes (number of women, men in each class): A (24, six); B (44, 13); C (43, 48); D (28, 17); and E (13, nine). Conclusions Compared with height-adjusted liver volume, the use of height-adjusted liver cystic volume showed greater separations in volumetric progression of polycystic liver disease. Similar to the Mayo imaging classification for the kidney, the progression of polycystic liver disease may be categorized on the basis of patient’s age and height-adjusted liver cystic volume.

BACKGROUND A treatment option for autosomal dominant polycystic kidney disease (ADPKD) has highlighted the need to identify rapidly progressive patients. Kidney size/age and genotype have predictive power for renal outcomes, but their relative and additive value, plus associated trajectories of disease progression, are not well defined. METHODS The value of genotypic and/or kidney imaging data (Mayo Imaging Class; MIC) to predict the time to functional (end-stage kidney disease [ESKD] or decline in estimated glomerular filtration rate [eGFR]) or structural (increase in height-adjusted total kidney volume [htTKV]) outcomes were evaluated in a Mayo Clinic PKD1/PKD2 population, and eGFR and htTKV trajectories from 20–65 years of age were modeled and independently validated in similarly defined CRISP and HALT PKD patients. RESULTS Both genotypic and imaging groups strongly predicted ESKD and eGFR endpoints, with genotype improving the imaging predictions and vice versa; a multivariate model had strong discriminatory power (C-index = 0.845). However, imaging but not genotypic groups predicted htTKV growth, although more severe genotypic and imaging groups had larger kidneys at a young age. The trajectory of eGFR decline was linear from baseline in the most severe genotypic and imaging groups, but it was curvilinear in milder groups. Imaging class trajectories differentiated htTKV growth rates; severe classes had rapid early growth and large kidneys, but growth later slowed. CONCLUSION The value of imaging, genotypic, and combined data to identify rapidly progressive patients was demonstrated, and reference values for clinical trials were provided. Our data indicate that differences in kidney growth rates before adulthood significantly define patients with severe disease. FUNDING NIDDK grants: Mayo DK058816 and DK090728; CRISP DK056943, DK056956, DK056957, and DK056961; and HALT PKD DK062410, DK062408, DK062402, DK082230, DK062411, and DK062401.

Significance Statement Accurate prediction of risk for disease progression is crucial for clinical management of autosomal dominant polycystic kidney disease (ADPKD). The Mayo imaging classification of ADPKD uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk. Because the current Mayo classification applies only to patients with typical diffuse cystic disease (class 1) and poorly predicts eGFR decline for the remaining 5%–10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. The authors report an expanded imaging classification model in which use of a recalculated htTKV value that excludes prominent exophytic cysts improved prediction for eGFR trajectory. Using a recalculated htTKV may allow inclusion of class 2 patients in the Mayo classification of ADPKD and reclassification of class 1 patients with prominent exophytic cysts. Background The Mayo Clinic imaging classification of autosomal dominant polycystic kidney disease (ADPKD) uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk for disease progression. However, this classification applies only to patients with typical diffuse cystic disease (class 1). Because htTKV poorly predicts eGFR decline for the 5%–10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. Methods Of 558 adults with ADPKD in the HALT-A study, we identified 25 patients of class 2A with prominent exophytic cysts (class 2Ae) and 43 patients of class 1 with prominent exophytic cysts; we recalculated their htTKVs to exclude exophytic cysts. Using original and recalculated htTKVs in association with imaging classification in logistic and mixed linear models, we compared predictions for developing CKD stage 3 and for eGFR trajectory. Results Using recalculated htTKVs increased specificity for developing CKD stage 3 in all participants from 82.6% to 84.2% after adjustment for baseline age, eGFR, BMI, sex, and race. The predicted proportion of class 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better calibrated to the observed value of 13.0% with recalculated htTKVs (45.5%) versus original htTKVs (63.6%). Using recalculated htTKVs reduced the mean paired difference between predicted and observed eGFR from 17.6 (using original htTKVs) to 4.0 ml/min per 1.73 m 2 for class 2Ae, and from −1.7 (using original htTKVs) to 0.1 ml/min per 1.73 m 2 for class 1. Conclusions Use of a recalculated htTKV measure that excludes prominent exophytic cysts facilitates inclusion of class 2 patients and reclassification of class 1 patients in the Mayo classification model.