Abstract
Predicting renal outcome in antineutrophil cytoplasmic antibody (ANCA)–associated glomerulonephritis (GN) remains a major challenge. We aimed to identify reliable predictors of end-stage renal disease (ESRD) and to develop and validate a clinicopathologic score to predict renal outcome in ANCA-associated GN. In a prospective training cohort of 115 patients, the percentage of normal glomeruli (without scarring, crescents, or necrosis within the tuft) was the strongest independent predictor of death-censored ESRD. Regression tree analysis identified predictive cutoff values for three parameters: percentage normal glomeruli (N0 >25%, N1 10 to 25%, N2 <10%), percentage tubular atrophy and interstitial fibrosis (T0 ≤25%, T1 >25%), and estimated glomerular filtration rate at the time of diagnosis (G0 >15 ml/min/1.73 m2, G1 ≤15 ml/min/1.73 m2). Cox regression analysis was used to assign points to each parameter (N1 = 4, N2 = 6, T1 = 2, G1 = 3 points), and the resulting risk score was used to classify predicted ESRD risk as low (0), intermediate (2 to 7), or high (8 to 11 points). The risk score accurately predicted ESRD at 36 months in the training cohort (0%, 26%, and 68%, respectively) and in an independent validation cohort of 90 patients (0%, 27%, and 78%, respectively). Here, we propose a clinically applicable renal risk score for ANCA-associated GN that highlights the importance of unaffected glomeruli as a predictor of renal outcome and allows early risk prediction of ESRD.
| Original language | English |
|---|---|
| Journal | Kidney International |
| Volume | 94 |
| Issue number | 6 |
| Pages (from-to) | 1177-1188 |
| Number of pages | 12 |
| ISSN | 0085-2538 |
| DOIs | |
| Publication status | Published - 12.2018 |
Funding
This work was supported by grants from the Deutsche Forschungsgemeinschaft ( KFO 228 STA193/9-1 and STA193/9-2 to RAKS and SFB 1192 C1 and B6 to RAKS and TW), and SRB was a research fellow employed by Project C1 of the SFB 1192. We thank Ulrike Langbehn and her team for the excellent technical work. We acknowledge the study nurses Birgit Goldmann, Eugen Kinzler, Samaneh Liagos, and Julia Fehlert for their thorough and dedicated collection of clinical data. We also acknowledge the statistical help of Ronald Simon. We also thank the following participating physicians: J. Steinhoff, G. Wolf, M.K. Kuhlmann, J. Beige, C.S. Haas, G.v.d. Loo, K. Grass, M. Rösch, S. Böse, T. Kahlert, F. Kunigk, D. Eitze, F. Lange-Hüsken, F. Köstler, S. Pawlow-Handt, M. Weiß, S. Mees, R. Weitzell, D. Duvigneau, W. Meyer, L. Arndt, H. Altrogge, J. Wogan, H.G. Wullstein, H.D. Fernow, W. Gompf, K. Meßtorff, A. und B. Born, T. Gohlke, J. Bachmann, C. Hülst, S. Tietz, B. Perras, S. Köhler, U. Dieball, F. Köstler, P. Jahn, L. Rohland, B. Ruhberg, C. Assmus, G. Marienhagen, C. Kuhlmann-Eilers, S. Abshagen, C. Harnisch, J. Masselmann, D. Zolotov, G. Becker, E. Rensinghoff, A. Trautmann, M. Spangenberg, W. Stolle, B. Pfeiffer-Zeller, R. Abu-Daher, J. Fleck, H. Hain, A. Pustelnick, K. Bestvater, M. Schröder, B. Kaltenmaier, U. Jannert, C. Braun, E. Eger, H.J. Ludwig, K.v. Appen, A. Klemm, U. Hoffmann, G. Wirtz, C. Pätzold, T. Meiners, U. Stauf, M. Schulte-Vorwick, S. Braun, H. Bücker, R. Groddeck, D. Brückner, K.D. Tamm, K. Rickels, S. Mehnert-Aner, G. Vollgraf, and U. Neuhäuser-Piduhn.
