ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

Please note that you are viewing an archived section from 2020 and some content may be unavailable. To unlock all content for 2020, please visit the archives.

Abstract: PO2357

Clinical and Biopsy Characteristics in a Pediatric Cohort of C3 Glomerulopathy (C3G) and Immune Complex Membranoproliferative Glomerulonephritis (IC-MPGN)

Session Information

Category: Pediatric Nephrology

  • 1700 Pediatric Nephrology

Authors

  • Dixon, Bradley P., Renal Section, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, United States
  • Goodwin Davies, Amy, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Razzaghi, Hanieh, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Meloni, Sherin, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Thomas, Melissa E., The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Flynn, Joseph T., Seattle Children's Hospital, Seattle, Washington, United States
  • Claes, Donna J., Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
  • Mitsnefes, Mark, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
  • Stotter, Brian Ross, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, United States
  • Dharnidharka, Vikas R., Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, United States
  • Gluck, Caroline A., Nemours/AI Dupont Hospital for Children, Wilmington, Delaware, United States
  • Zaritsky, Joshua, Nemours/AI Dupont Hospital for Children, Wilmington, Delaware, United States
  • Somers, Michael J., Boston Children's Hospital, Boston, Massachusetts, United States
  • Kallash, Mahmoud, Nationwide Children's Hospital, Columbus, Ohio, United States
  • Smoyer, William E., Nationwide Children's Hospital, Columbus, Ohio, United States
  • Furth, Susan L., The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Forrest, Christopher B., The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Laskin, Benjamin L., The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
  • Denburg, Michelle, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
Background

C3G and IC-MPGN are rare diseases. The ability to identify and phenotype children with C3G and IC-MPGN using electronic health records (EHR) would aid description of natural history and prognosticate therapeutic response.

Methods

Using a computable phenotype algorithm, a pediatric cohort of children with glomerular disorders was identified in PEDSnet, a national network of pediatric health systems with aggregated EHR data, and refined using MPGN-specific SNOMED-CT codes to identify C3G and IC-MPGN patients at 6 centers. Discrete data elements were captured from electronic health records, and additional clinical data were extracted by standardized chart review. Biopsy diagnosis was classified as C3G or IC-MPGN by applying an automated algorithm to immunofluorescence data.

Results

Of 285 identified patients, 173 were true cases of C3G or IC-MPGN (Tables 1 and 2). Median C3 level at diagnosis was lower in C3G compared to IC-MPGN (p=0.005). There were no significant differences in light microscopic injury pattern or ultrastructure between C3G and IC-MPGN biopsies, but C3 intensity was higher in C3G compared to IC-MPGN (p = 0.006) (Table 3).

Conclusion

Patients with C3G and IC-MPGN can be identified and characterized by the use of a computable phenotype, allowing the creation of robust databases to define clinical predictors of treatment response. This may prove to be a vital asset for recruitment into clinical trials of complement-targeted agents likely beneficial to this patient population.

Funding

  • NIDDK Support –