Classification System for Predicting Outcomes in ADPKD

The Mayo imaging classification (MIC) tool was created to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD). Thomas Bais and other researchers working on behalf of the DIPAK consortium evaluated MIC to validate its ability to predict kidney outcomes in a large, multicenter ADPKD cohort. They included patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 estimated glomerular filtration rate (eGFR) values during ≥1 year follow-up. 

Measurements included Mayo HtTKV class stability, kidney growth rates, and eGFR rates of decline. The research team compared the observed eGFR decline with predictions from the Mayo Clinic future eGFR equation and tested the future eGFR prediction equation for nonlinear eGFR decline. They used Kaplan-Meier survival analysis and Cox regression models to evaluate time to kidney failure using Mayo HtTKV class as a predictor variable. 

The study included 618 patients with a mean age of 47 ± 11 years and mean eGFR of 64 ± 25 mL/min/1.73 m2 at baseline. Most (82%) stayed in their baseline Mayo HtTKV class. During a mean follow-up of 5.1 ± 2.2 years, mean TKV growth rates and eGFR decline were 5.33 ± 3.90 %/year and −3.31 ± 2.53 mL/min/1.73 m2/year, respectively. Kidney growth and eGFR decline overlapped considerably between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D, but it was significantly slower for class 1E.  

Ninety-seven (16%) patients developed kidney failure during follow-up. MIC predicted the development of kidney failure, although the sensitivity and positive predictive values were limited. Although the MIC showed acceptable stability and predicted kidney failure and eGFR decline rate, there was considerable interindividual variability in the rate of disease progression within each class.

Source: Clinical Journal of the American Society of Nephrology