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Record W4416871675 · doi:10.2215/cjn.0000000881

Longitudinal Trajectories of Kidney Function in Patients with Severe ANCA-Associated Vasculitis

2025· article· en· W4416871675 on OpenAlexafffund
Jennifer S. Hanberg, Yuqing Zhang, Andreas Kronbichler, Anna Matyjek, Carol A. McAlear, Balazs Odler, Lisa Uchida, Duvuru Geetha, Carmel M. Hawley, David Jayne, Michael Walsh, Peter A. Merkel, Zachary S. Wallace

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsHamilton Health SciencesImpactMcMaster UniversityPopulation Health Research Institute
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Health and Medical Research CouncilNational Institutes of HealthCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchAssistance Publique - Hôpitaux de ParisRheumatology Research FoundationMassachusetts General Hospital
KeywordsKidney diseaseVasculitisRenal functionConfidence intervalOdds ratioCohortCohort studyKidney

Abstract

fetched live from OpenAlex

Key Points We identified four kidney function trajectories in ANCA-associated vasculitis associated with varied risk of ESKD and CKD. Male sex, myeloperoxidase-ANCA specificity, baseline hypertension, and lower baseline eGFR were associated with adverse kidney function trajectory. Differences in baseline characteristics identify potential targets for personalized care. Background ANCA-associated vasculitis (AAV) causes GN culminating in a spectrum of CKD and ESKD. Kidney function trajectories with differing CKD risk were reported in a single-center study. We sought to confirm these findings in the Plasma Exchange and Glucocorticoids in Severe AAV trial cohort. Methods This cohort study included patients from Plasma Exchange and Glucocorticoids in Severe AAV with a baseline and one or more eGFR measurements over 36 months to identify groups with similar longitudinal change in kidney function using group-based trajectory analysis. We evaluated factors associated with group membership using multivariable-adjusted multinomial logistic regression. Results We identified four trajectory groups among 663 patients: early ESKD (113, 17%), stable impairment (400, 60%), impaired with improvement (131, 20%), and marked improvement (19, 3%). ESKD occurred in 137 patients (21%): 113 (100%) in the early ESKD, 23 (6%) in the stable impairment, and 1 (1%) in the impaired with improvement groups. Older baseline age (adjusted odds ratio [aOR], 0.97 per 1 year; 95% confidence interval [CI], 0.95 to 0.99), female sex (aOR, 0.54; 95% CI, 0.31 to 0.94), and baseline eGFR (aOR, 0.93 per 1 ml/min per 1.73 m 2 increase; 95% CI, 0.88 to 0.99) were associated with lower odds of early ESKD versus impaired with improvement membership. Myeloperoxidase-ANCA+ (aOR, 2.35; 95% CI, 1.33 to 4.16) and hypertension (aOR, 2.50; 95% CI, 1.33 to 4.69) were associated with higher odds of early ESKD membership. Conclusions We identified four kidney function trajectories in AAV associated with varied risk of ESKD and CKD; male sex, myeloperoxidase-ANCA specificity, baseline hypertension, and lower baseline eGFR were associated with adverse kidney function trajectory. Differences in baseline characteristics identify potential targets for personalized care.

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)0.005
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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