• 1.

    Jennette C, et al. 2012 Revised International Chapel Hill Consensus Conference nomenclature of vasculitides. Arthritis Rheum 2013; 65:111. doi: 10.1002/art.37715

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2.

    Segelmark M, Hellmark T. Anti-glomerular basement membrane disease: An update on subgroups, pathogenesis and therapies. Nephrol Dial Transplant 2019; 34:18261832. doi: 10.1093/ndt/gfy327

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Savage CO, et al. Antiglomerular basement membrane antibody mediated disease in the British Isles 1980-4. Br Med J (Clin Res Ed) 1986; 292:301304. doi: 10.1136/bmj.292.6516.301

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Marques C, et al. Prognostic factors in anti-glomerular basement membrane disease: A multicenter study of 119 patients. Front Immunol 2019; 10:1665. doi: 10.3389/fimmu.2019.01665

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    van Daalen EE, et al. Predicting outcome in patients with anti-GBM glomerulonephritis. Clin J Am Soc Nephrol 2018; 13:6372. doi: 10.2215/CJN.04290417

  • 6.

    Alchi B, et al. Predictors of renal and patient outcomes in anti-GBM disease: Clinicopathologic analysis of a two-centre cohort. Nephrol Dial Transplant 2015; 30:814821. doi: 10.1093/ndt/gfu399

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Rovin BH, et al. KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int 2021; 100:S1S276. doi: 10.1016/j.kint.2021.05.021

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Brix SR, et al. Development and validation of a renal risk score in ANCA-associated glomerulonephritis. Kidney Int 2018; 94:11771188. doi: 10.1016/j.kint.2018.07.020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Floyd L, et al. Risk stratification to predict renal survival in anti-GBM disease. J Am Soc Nephrol (published online ahead of print November 29, 2022). doi: 10.1681/ASN.2022050581; https://journals.lww.com/jasn/Abstract/9900/Risk_Stratification_to_Predict_Renal_Survival_in.71.aspx

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Clinicopathologic Predictors of Prognosis in Anti-Glomerular Basement Membrane Disease—Can We Do Better?

Nasim WiegleyNasim Wiegley, MD, FASN, is an assistant professor of medicine with the University of California Davis School of Medicine, Sacramento, CA. Ana Naidas, MD, is a nephrology consultant with the Capitol Medical Center, Quezon City, Metro Manila, Philippines.

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Ana NaidasNasim Wiegley, MD, FASN, is an assistant professor of medicine with the University of California Davis School of Medicine, Sacramento, CA. Ana Naidas, MD, is a nephrology consultant with the Capitol Medical Center, Quezon City, Metro Manila, Philippines.

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Anti-glomerular basement membrane (anti-GBM) disease is a rare autoimmune disease with an incidence of 0.5–1 per million population. The pathogenic autoantibodies target the non-collagenous domain of the α3 chain of type IV collagen found in the basement membrane of the glomeruli in the kidneys and alveolar capillary walls in the lungs, leading to significant organ injury with a high risk of morbidity and mortality. Patients commonly present with rapidly progressive glomerulonephritis, at times accompanied by alveolar capillaritis and pulmonary hemorrhage (1, 2). Over the past decades, aggressive treatments, such as plasma exchange for rapid removal of the pathogenic antibody and immunosuppression with glucocorticoids and cyclophosphamide, have played a crucial role in improving patient survival outcomes (3), with post-treatment 5-year patient survival reaching >90% (4). However, kidney survival remains suboptimal, with many patients progressing to end stage kidney disease (ESKD) (4, 5).

Considering the aggressive nature of this disease, improving our prognostication can aid in individualized care to enhance patient-related outcomes while reducing treatment-related adverse events. Oligoanuria and dialysis dependence at presentation have been previously associated with poor patient and kidney outcomes (6). Therefore, recent Kidney Disease: Improving Global Outcomes (KDIGO) glomerular disease guidelines recommend withholding aggressive immunosuppressive therapy in patients with a dialysis need at presentation, crescents in 100% of glomeruli sampled, or >50% glomerulosclerosis on kidney biopsy in the absence of pulmonary hemorrhage (7) to reduce unnecessary medication-related toxicity. To improve outcomes for these complex and vulnerable patients, there is a great need for better risk-stratification tools and an improved understanding of clinicopathologic predictors of outcome to aid informed decision-making and individualized treatment approaches.

[I]mproving our prognostication can aid in individualized care … while reducing treatment-related adverse events.

The renal risk score (RRS) was initially developed as a prediction tool for anti-neutrophil cytoplasm antibody-associated vasculitis (8); however, to date, there have not been any dedicated risk-stratification tools for anti-GBM disease. A recent retrospective cohort study by Floyd et al. (9) aimed to further investigate various clinicopathologic factors that can aid in identifying patients who might benefit from immunosuppressive therapy, despite aggressive disease at presentation, and validate the use of RRS for anti-GBM disease. A total of 174 patients with biopsy-proven anti-GBM disease from seven European kidney-referral centers and registries were included in this study, a subset of whom required dialysis support on presentation. Interestingly, this study showed that the RRS is usable for risk stratification in anti-GBM disease as well (Harrell's C = 0.760; 95% confidence interval [CI], 0.69–0.83; p < 0.001). In multivariate analysis, the combination of the need for kidney replacement therapy (KRT) at diagnosis and the percentage of normal glomeruli in histopathology were independent predictors for ESKD. On further analysis, the presence of 10% normal glomeruli in the biopsy separated kidney outcomes and rate of recovery. Patients with 10% or more normal glomeruli on biopsy had a higher rate of kidney recovery, even if they were initially dialysis dependent on presentation. In comparison, dialysis-independent patients with little or no normal glomeruli (<10%) developed ESKD more often.

In line with this, a bivariable prediction model composed of these two factors (initial KRT need and percentage of normal glomeruli) yielded superior discrimination for long-term kidney survival compared with the RRS alone (C = 0.840; 95% CI, 0.79–0.89; p < 0.001). In this study, a sensitivity analysis of biopsies with at least 10 glomeruli (134/174) did not detect a significant difference in the performance of this tool (C = 0.820). However, since this prediction model is based on the number of normal glomeruli in kidney biopsy, caution is needed in analyzing biopsies with <10 glomeruli.

Future prospective validation of this prediction tool would be valuable, although the rarity of anti-GBM disease will make this task difficult. Overall, the added prognostic information provided by prediction tools can aid in identifying individuals with a good potential for kidney recovery and improve patient-related outcomes based on an individualized treatment approach. In addition, although the utility of kidney biopsy in oligoanuric patients with anti-GBM disease has been questioned in the past (6), the results of this study shed light on the additive value of histologic information in risk stratification, further highlighting the benefits of individualizing care.

References

  • 1.

    Jennette C, et al. 2012 Revised International Chapel Hill Consensus Conference nomenclature of vasculitides. Arthritis Rheum 2013; 65:111. doi: 10.1002/art.37715

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2.

    Segelmark M, Hellmark T. Anti-glomerular basement membrane disease: An update on subgroups, pathogenesis and therapies. Nephrol Dial Transplant 2019; 34:18261832. doi: 10.1093/ndt/gfy327

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    Savage CO, et al. Antiglomerular basement membrane antibody mediated disease in the British Isles 1980-4. Br Med J (Clin Res Ed) 1986; 292:301304. doi: 10.1136/bmj.292.6516.301

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Marques C, et al. Prognostic factors in anti-glomerular basement membrane disease: A multicenter study of 119 patients. Front Immunol 2019; 10:1665. doi: 10.3389/fimmu.2019.01665

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    van Daalen EE, et al. Predicting outcome in patients with anti-GBM glomerulonephritis. Clin J Am Soc Nephrol 2018; 13:6372. doi: 10.2215/CJN.04290417

  • 6.

    Alchi B, et al. Predictors of renal and patient outcomes in anti-GBM disease: Clinicopathologic analysis of a two-centre cohort. Nephrol Dial Transplant 2015; 30:814821. doi: 10.1093/ndt/gfu399

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Rovin BH, et al. KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int 2021; 100:S1S276. doi: 10.1016/j.kint.2021.05.021

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Brix SR, et al. Development and validation of a renal risk score in ANCA-associated glomerulonephritis. Kidney Int 2018; 94:11771188. doi: 10.1016/j.kint.2018.07.020

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Floyd L, et al. Risk stratification to predict renal survival in anti-GBM disease. J Am Soc Nephrol (published online ahead of print November 29, 2022). doi: 10.1681/ASN.2022050581; https://journals.lww.com/jasn/Abstract/9900/Risk_Stratification_to_Predict_Renal_Survival_in.71.aspx

    • Search Google Scholar
    • Export Citation
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