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

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). Table 1. - Patient Characteristics Total adjudicated MPGN cases C3G IC-MPGN N 173 59 71 Age at diagnosis (years) 0-4 7 (4.1%) 3 (5.1%) 2 (2.8%) 5-9 48 (28%) 17 (29%) 20 (28%) 10-14 68 (39%) 26 (44%) 18 (25%) 15-20 48 (28%) 12 (20%) 19 (27%) 20+ 2 (1.2%) 1 (1.7%) 0 Follow-up time since diagnosis (years) 5.2 (2.6, 8.1) 5.8 (2.5, 8.1) 4.6 (2.7, 7.8) # of nephrology visits (per person-year) 3.4 (1.8, 7.0) 3.1 (1.8, 5.7) 4.1 (2.7, 7.1) Dialysis 16 (9.3%) 4 (6.8%) 3 (4.2%) Kidney transplant 12 (6.9%) 3 (5.1%) 2 (2.8%) Serum albumin (g/dL) 3.2 (2.4, 4.0) 3.0 (2.4, 4.0) 2.5 (2.1, 3.7) Complement C3 (mg/dL) 42.1 (17, 89) 27.5 (12, 53.8) 42.5 (22, 102) Table 2. - Therapeutics Total adjudicated MPGN cases C3G IC-MPGN ACE inhibition 121 (70%) 43 (73%) 42 (59%) Anglotensin receptor blockade 54 (31%) 17 (29%) 22 (31%) Immunosuppression/Immunomodulatory Corticosterold 130 (75%) 44 (75%) 48 (68%) Mycophenolate 74 (43%) 30 (51%) 23 (32%) Calcineurin inhibitor 38 (22%) 9 (15%) 13 (18.3%) Cyclophosphamide 2 (1.2%) 0 1 (1.4%) Azathloprine 11 (6.4%) 5 (8.5%) 3 (4.2%) Eculizumab 17 (9.8%) 10 (17%) 5 (7.0%) Table 3. - Biopsy Characteristics C3G IC-MPGN N 75 92 Light microscopy Membranoproliferative pattern 49 (66%) 69 (75%) Mesangloproliferative pattern 28 (37%) 43 (47%) Endocapillary proliferative pattern 18 (24%) 30 (33%) Crescents 16 (22%) 26 (28%) Globally sclerotic glomerulli 32 (43%) 29 (32%) C3 Intensity on Immunofluorescence <2+ 4 (5%) 19 (21%) 2+/2-3+ 11 (15%) 21 (23%) 3+/3-4+ 37 (49%) 34 (37%) 4+ 23 (31%) 18(20%) Electron Microscopy Mesangial deposits 59 (81%) 64 (73%) Subendothelial deposts 47 (64%) 63 (72%) Intramembranous deposits 42 (58%) 44 (50%) Subepithelial deposits 27 (37%) 31 (35%) Data are reported as n (% of cohort), or median values with Interquartile range where applicable. Conclusions: 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, Commercial Support - Mallinckrodt Pharmaceuticals