MétaCan
Menu
Back to cohort
Record W2053231382 · doi:10.1002/art.38421

A10: Younger Age and Severity of Renal Presentation Distinguishes Microscopic Polyangiitis From Granulomatosis With Polyangiitis in Children: An ARChiVe Study

2014· article· en· W2053231382 on OpenAlexaff
Debra Bingham, Eyal Muscal, Kabita Nanda, Dawn M. Wahezi, Steven J. Spalding, Marinka Twilt, Susanne M. Benseler, David A. Cabral

Bibliographic record

VenueArthritis & Rheumatology · 2014
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of British ColumbiaHospital for Sick Children
Fundersnot available
KeywordsGranulomatosis with polyangiitisPresentation (obstetrics)Microscopic polyangiitisMedicinePathologyDermatologyVasculitisDiseaseSurgery

Abstract

fetched live from OpenAlex

Background/Purpose: Comparisons of pediatric ANCA‐associated vasculitis subtypes (AAV) are limited by the paucity of reported cases, standardized definitions, and overlapping classification criteria. Published work from ARChiVe (A Registry for Childhood Vasculitis) demonstrated modifications of validated classification algorithms applied to pediatric patients with AAV can classify each patient to mutually exclusive diagnostic categories. We compared presenting features of children with microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA) classified according to this methodology. Methods: A pediatric modification of the European Medicines Agency (EMA) algorithm for classifying AAV and polyarteritis nodosa (incorporating the EULAR/PRINTO/PRES pediatric classification criteria for GPA) was applied to patients in ARChiVe censored to April 2012. We compared characteristics of patients classified as having MPA and GPA. STATA (Statcorp, 2013) was used to calculate frequencies, percentages, and chi‐squared with fisher's exact for categorical variables and means, standard deviations, and t‐tests for continuous variables. Results: One hundred fifty‐two of 227 children in ARChiVe met criteria for diagnosis of MPA (n = 22) or GPA (n = 130). Characteristics and presenting features are shown in Table . Children with MPA were younger at diagnosis (mean diff. 2.7y, p = <0.01). Renal involvement was predominant in both groups. Renal biopsies in 40% of both groups were consistent with pauci‐immune, necrotizing glomerulonephritis. Children with MPA had higher rates of nephrosis, renal failure requiring dialysis, and abnormal creatinine clearance (Table ). Upper and lower airway involvement was more prevalent among those with GPA largely in accordance with surrogate GPA features used to differentiate GPA and MPA in the EMA algorithm. The majority of patients presented with constitutional symptoms, however, other organ systems were less frequently involved. Most patients received combination therapy corticosteroids and cytoxan (64% MPA, 81% GPA) with additional plasmapheresis (29% MPA, 21% GPA), rituximab (14% MPA, 3% GPA) or methotrexate (7% MPA, 1% GPA). The remainder of children received combination corticosteroids and methotrexate or rituximab, without cytoxan (12% MPA, 11% GPA). A larger proportion of patients with MPA received antihypertensive agents and/or ACE inhibitors (64% vs 35%, p = 0.01). Characteristics & Presenting Clinical Features of children with microscopic polyangiitis or granulomatosis with polyangiitis in the ARChiVe cohort (n = 152) Algorithm‐derived diagnosis Characteristic/Feature MPA (n = 22) GPA (n = 130) p‐value Female, n(%) 15 (68) 83 (64) 0.81 Caucasian, n (%) 11 (50) 85 (66) 0.231 Age at diagnosis, yrs, mean (sd) 11.6 (5) 14.2 (3) <0.01* Median (range) 12.8 (9–15) 14.9 (4–19) Symptom duration prior to diagnosis, mos, mean (sd) 4.4 (9) 5.2 (10) 0.72 Median (range) 3.4 (0–35.8) 4.8 (0–66.8) MD‐assigned diagnosis (clinical diagnosis) MPA or isolated MPA 8 (36) 18 (14) 0.027* WG or limited WG 7 (32) 107 (82) <0.01* ANCA 3 (14) 1 (1) 0.01* Unclassified 4 (18) 4 (3) 0.02* General Features Fatigue 19 (86) 113 (87) 1.00 Fever 10 (46) 71 (55) 0.49 Weight loss 10 (46) 60 (46) 1.00 Renal 18 (82) 103 (79) 1.00 Hypertension 8 (36) 28 (22) 0.17 Hematuria and proteinuria with red blood cell casts 16 (73) 97 (75) 0.80 Nephrotic range proteinuria with edema 6 (27) 14 (11) 0.04* Renal failure requiring dialysis 7 (32) 17 (13) 0.05* Creatinine clearance >25% lower limit of normal 8 (89) 18 (36) <0.01* Biopsy‐proven glomerulonephritis 6 (27) 28 (22) 0.58 Pulmonary 9 (41) 105 (81) <0.01* Chronic cough 6 (27) 79 (61) <0.01* Shortness of breath 4 (18) 63 (49) 0.01* Hemoptysis/alveolar hemorrhage 3 (14) 54 (42) 0.02* Fixed pulmonary infiltrates ± cavitations, nodules 4 (18) 99 (76) <0.01* Head, Ear, Nose, & Throat 4 (18) 97 (75) <0.01* Bloody nasal discharge ± crusting 0 (0) 68 (52) <0.01* Chronic sinusitis, otitis, or mastoiditis 0 (0) 63 (48) <0.01* Subglottic involvement 0 (0) 16 (12) 0.13 Cranial bone ± cartilage destruction 0 (0) 10 (8) 0.36 Acute hearing loss 0 (0) 15 (12) 0.13 Red ± painful eye conditions 2 (9) 34 (26) Gastrointestinal/Abdominal 15 (68) 51 (39) 0.01* Chronic nausea 9 (41) 17 (13) <0.01* Skin 10 (45) 71 (55) 0.49 Musculoskeletal 80 (62) 14 (64) 1.00 Nervous System 35 (27) 6 (27) 1.00 Cardiovascular 9 (7) 1 (5) 1.00 ANCA Serology pANCA or anti‐MPO 14 (70) 35 (29) <0.01 cANCA or anti‐PR3 6 (30) 86 (71) <0.01 Conclusion: Children with AAV had predominantly renal and constitutional manifestations. Younger age and a more severe renal disease phenotype may characterize MPA with patients requiring additional treatment for the consequences of kidney disease. The wide variations in time to diagnosis continue to suggest that pediatric AAV is poorly recognized. Ongoing biomarker‐driven studies may complement systems for subclassifying patients with

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.229
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueArthritis & RheumatologySame topicVasculitis and related conditionsFrench-language works237,207