Predicting Future Renal Function Decline in Patients with Autosomal Dominant Polycystic Kidney Disease Using Mayo Clinic Classification

Introduction: Mayo clinic classification (MCC) has been proposed in patients with autosomal dominant polycystic kidney disease (ADPKD) to identify who may experience a rapid decline of renal function. Our aim was to validate this predictive model in a population from southern Spain. Methods: ADPKD patients with measurements of height-adjusted total kidney volume (HtTKV) and baseline estimated glomerular filtration rate (eGFR) >30 mL/min/1.73 m(2) were selected. Last eGFR was estimated with Mayo Clinic (MC) equation and bias and accuracy were studied. We also analyzed predictive capacity of MCC classes using survival analysis and Cox regression models. Results: We included 134 patients with a mean follow-up of 82 months. While baseline eGFR was not different between classes, last eGFR decreased significantly with them. eGFR variation rate was different according to the MCC class with a more rapid decline in 1C, 1D, and 1E classes. Final eGFR predicted was not significantly different from the real one, with an absolute bias of 0.6 +/- 17.0 mL/min/1.73 m(2). P10 accuracy was low ranging from 37.5 to 59.5% in classes 1C, 1D, and 1E. Using MC equation, the rate of eGFR decline was underestimated in 1C, 1D, and 1E classes. Cox regression analysis showed that MCC class is a predictor of renal survival after adjusting with baseline eGFR, age, sex, and HtTKV, with 1D and 1E classes having the worst prognosis. Conclusion: MCC classification is able to identify patients who will undergo a more rapid decline of renal function in a Spanish population. Prediction of future eGFR with MC equation is acceptable as a group, although it shows a loss of accuracy considering individual values. The rate of eGFR decline calculated using MC equation can underestimate the real rate presented by patients of 1C, 1D, and 1E classes.