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Published on: June 23, 2015
Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD
Thomas Bais1, Paul Geertsema1, Martine G E Knol1
1Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Insights
The Mayo Imaging Classification generally predicts kidney disease progression in autosomal dominant polycystic kidney disease (ADPKD) patients, but individual variability exists. This validation study confirms its utility in predicting kidney failure and eGFR decline rates.
Area of Science:
- Nephrology
- Medical Imaging
- Genetics
Background:
- The Mayo Imaging Classification aids in predicting autosomal dominant polycystic kidney disease (ADPKD) progression.
- Validation of this classification in a large, multicenter ADPKD cohort is crucial for clinical application.
Purpose of the Study:
- To validate the Mayo Imaging Classification's ability to predict kidney outcomes in a diverse ADPKD patient population.
- To assess the stability and predictive accuracy of the classification regarding kidney growth and estimated glomerular filtration rate (eGFR) decline.
Main Methods:
- Utilized data from 618 ADPKD patients with height-adjusted total kidney volume (HtTKV) and longitudinal eGFR measurements.
- Calculated HtTKV class stability, kidney growth rates, eGFR decline rates, and compared observed eGFR decline with Mayo Clinic predictions.
- Employed survival analysis (Kaplan-Meier) and Cox regression to evaluate kidney failure prediction based on Mayo HtTKV class.
Main Results:
- 82% of patients remained in their baseline Mayo HtTKV class, indicating acceptable stability.
- Mean annual kidney volume growth was 5.33% and mean eGFR decline was -3.31 ml/min/1.73 m²/year, with overlap between classes.
- The classification predicted kidney failure (16% incidence) but showed limited sensitivity and positive predictive values; nonlinear models improved eGFR predictions.
Conclusions:
- The Mayo Imaging Classification demonstrates acceptable stability and general predictive capability for kidney failure and eGFR decline in ADPKD.
- Significant interindividual variability in disease progression rates within each classification category necessitates careful clinical interpretation.
Background:
The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort.
Methods:
Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 eGFR values during ≥1-year follow-up. Mayo HtTKV class stability, kidney growth rates, and eGFR decline rates were calculated. The observed eGFR decline was compared with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear eGFR decline. Kaplan-Meier survival analysis and Cox regression models were used to assess time to kidney failure using Mayo HtTKV class as a predictor variable.
Results:
We analyzed 618 patients with a mean age of 47±11 years and mean eGFR of 64±25 ml/min per 1.73 m 2 at baseline. Most patients (82%) remained in their baseline Mayo HtTKV class. During a mean follow-up of 5.1±2.2 years, the mean total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per year, respectively. Kidney growth and eGFR decline showed considerable overlap between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D but significantly slower for class 1E. This was also observed in patients aged younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear eGFR decline provided more accurate slope predictions. Ninety-seven patients (16%) developed kidney failure during follow-up. The classification predicted the development of kidney failure, although the sensitivity and positive predictive values were limited.
Conclusions:
The Mayo Imaging Classification demonstrated acceptable stability and generally predicted kidney failure and eGFR decline rate. However, there was marked interindividual variability in the rate of disease progression within each class.
