Bae, Kyongtae Ty

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.

Abstract Background Patients with mild autosomal dominant polycystic kidney disease (ADPKD) are less likely to be informative in randomized clinical trials (RCTs). We previously developed an imaging classification of ADPKD (typical diffuse cyst distribution Class 1A–E and atypical cyst distribution Class 2) for prognostic enrichment design in RCTs. We investigated whether using this classification would have increased the power to detect a beneficial treatment effect of rigorous blood pressure (BP) control on HALT-PKD participants with early disease (Study A). Methods Post hoc analysis of the early disease HALT-PKD study, an RCT that studied the effect of rigorous versus standard BP control on rates of total kidney volume (TKV) increase and estimated glomerular filtration rate (eGFR) decline in ADPKD patients with eGFR &gt;60 mL/min/1.73 m2. Results Five hundred and fifty-one patients were classified by two observers (98.2% agreement) into Class 1A (6.2%), 1B (20.3%), 1C (34.1%), 1D (22.1%), 1E (11.8%) and 2 (5.4%). The TKV increase and eGFR decline became steeper from Class 1A through 1E. Rigorous BP control had been shown to be associated with slower TKV increase, without a significant overall effect on the rate of eGFR decline (faster in the first 4 months and marginally slower thereafter). Merging Classes 1A and 2 (lowest severity), 1B and 1C (intermediate severity) and 1D and 1E (highest severity) detected stronger beneficial effects on TKV increase and eGFR decline in Class 1D and E with a smaller number of patients. Conclusions Strategies for prognostic enrichment, such as image classification, should be used in the design of RCTs for ADPKD to increase their power and reduce their cost.