Risk Stratification to Predict Renal Survival in Anti–Glomerular Basement Membrane Disease

Lauren Floyd(Lancashire Teaching Hospitals NHS Foundation Trust), Sebastian Bate(Agricultural Development Advisory Service (United Kingdom)), Abdul Hadi Kafagi(Agricultural Development Advisory Service (United Kingdom)), Nina Brown(Agricultural Development Advisory Service (United Kingdom)), Jennifer Scott(Trinity College Dublin), Mukunthan Srikantharajah(Agricultural Development Advisory Service (United Kingdom)), Marek Mysliveček(Charles University), Graeme Reid(Agricultural Development Advisory Service (United Kingdom)), Faten Aqeel(Johns Hopkins University), Doubravka Frausová(Charles University), Marek Kollár(Agricultural Development Advisory Service (United Kingdom)), Phuong Le Kieu(Agricultural Development Advisory Service (United Kingdom)), Bilal Khurshid(Agricultural Development Advisory Service (United Kingdom)), Charles D. Pusey(Lancashire Teaching Hospitals NHS Foundation Trust), Ajay Dhaygude(Lancashire Teaching Hospitals NHS Foundation Trust), Vladimı́r Tesař(Charles University), Stephen P. McAdoo(Trinity College Dublin), Mark A. Little(Johns Hopkins University), Duvuru Geetha(Johns Hopkins University), Silke R. Brix(University of Manchester)
Journal of the American Society of Nephrology
November 29, 2022
Cited by 32Open Access
Full Text

Abstract

Significance Statement Most patients with anti–glomerular basement membrane (GBM) disease present with rapidly progressive glomerulonephritis, and more than half develop ESKD. Currently, no tools are available to aid in the prognostication or management of this rare disease. In one of the largest assembled cohorts of patients with anti-GBM disease (with 174 patients included in the final analysis), the authors demonstrated that the renal risk score for ANCA-associated vasculitis is transferable to anti-GBM disease and the renal histology is strongly predictive of renal survival and recovery. Stratifying patients according to the percentage of normal glomeruli in the kidney biopsy and the need for RRT at the time of diagnosis improves outcome prediction. Such stratification may assist in the management of anti-GBM disease. Background Prospective randomized trials investigating treatments and outcomes in anti–glomerular basement membrane (anti-GBM) disease are sparse, and validated tools to aid prognostication or management are lacking. Methods In a retrospective, multicenter, international cohort study, we investigated clinical and histologic parameters predicting kidney outcome and sought to identify patients who benefit from rescue immunosuppressive therapy. We also explored applying the concept of the renal risk score (RRS), currently used to predict renal outcomes in ANCA-associated vasculitis, to anti-GBM disease. Results The final analysis included 174 patients (out of a total of 191). Using Cox and Kaplan–Meier methods, we found that the RRS was a strong predictor for ESKD. The 36-month renal survival was 100%, 62.4%, and 20.7% in the low-risk, moderate-risk, and high-risk groups, respectively. The need for renal replacement therapy (RRT) at diagnosis and the percentage of normal glomeruli in the biopsy were independent predictors of ESKD. The best predictor for renal recovery was the percentage of normal glomeruli, with a cut point of 10% normal glomeruli providing good stratification. A model with the predictors RRT and normal glomeruli ( N ) achieved superior discrimination for significant differences in renal survival. Dividing patients into four risk groups led to a 36-month renal survival of 96.4% (no RRT, N ≥10%), 74.0% (no RRT, N <10%), 42.3% (RRT, N ≥10%), and 14.1% (RRT, N <10%), respectively. Conclusions These findings demonstrate that the RRS concept is transferrable to anti-GBM disease. Stratifying patients according to the need for RRT at diagnosis and renal histology improves prediction, highlighting the importance of normal glomeruli. Such stratification may assist in the management of anti-GBM disease. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2023_02_27_JASN0000000000000060.mp3


Related Papers

No related papers found

Powered by citation graph analysis