MétaCan
Menu
Back to cohort
Record W4415426922 · doi:10.1093/ndt/gfaf116.0212

#988 The impact of kidney relapse on long-term kidney function in ANCA-associated vasculitis

2025· article· en· W4415426922 on OpenAlexaff
Lisa Uchida, Vanja Ivković, Anna Matyjek, Duvuru Geetha, Balazs Odler, Carmel M. Hawley, Zachary S. Wallace, Carol A. McAlear, Mats Junek, Peter A. Merkel, Michael Walsh, David Jayne, Andreas Kronbichler

Bibliographic record

VenueNephrology Dialysis Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcMaster University
Fundersnot available
KeywordsRenal functionKidneyKidney diseaseVasculitisNephrology

Abstract

fetched live from OpenAlex

Abstract Background and Aims ANCA-associated vasculitis (AAV) often results in chronic kidney disease (CKD) and progresses to end-stage kidney disease (ESKD). Long-term trajectories of kidney function and the impact of kidney relapse on these trajectories in AAV are not well defined. This study evaluated long-term kidney function before and after kidney relapse and compared the trajectories to those of patients without relapse, using data from the PEXIVAS trial of plasma exchange and glucocorticoid dosing. Method This post-hoc analysis of the PEXIVAS trial included patients with kidney involvement at baseline who achieved remission prior to 12 months and had at least 2 subsequent estimated glomerular filtration rate (eGFR) assessments. Patients who developed ESKD before 12 months or those without eGFR data beyond 12 months were excluded. We calculated eGFR slopes of patients who did not experience kidney relapse as well as pre- and post-relapse slopes of patients who had kidney relapse at least 12 months after randomisation. We additionally compared the group mean eGFRs at each follow-up time. To create a common time to compare eGFR relative to the time of relapse, Time 0 was defined as: (i) time of relapse for relapsers and (ii) time corresponding to the month of relapse of their matched relapsers for non-relapsers. Patients were matched using propensity scores based on sex, ANCA subtype, eGFR at randomisation and time of enrolment. A linear mixed-effects model was employed to estimate eGFR slope, with adjustment for the main effects of age, sex, ANCA subtype, eGFR at randomisation, and their interactions with time. Results Of 704 participants in PEXIVAS, 459 were included, of whom 51 (11.1%) had a kidney relapse after 12 months (median time to relapse: 847 days). Baseline characteristics were comparable between those with and without kidney relapse, including median eGFR at randomisation (17.2 vs 19.2 ml/min/1.73 m2; P = 0.76) and at 12 months (41.3 vs 42.7 ml/min/1.73 m2; P = 0.37) (Table 1). The mean eGFR in patients that relapsed was lower at and after relapse compared to those that did not relapse (Fig. 1). The overall slope in the non-relapse group was 0.7 (95% CI: −0.3 to 1.8) mL/min/1.73 m2/year. In the relapse group, the pre-relapse slope was −5.9 (95% CI: −10.2 to −1.5) mL/min/1.73 m2/year, while the post-relapse slope was −1.1 (95% CI: −3.8 to 1.6) mL/min/1.73 m2/year. After adjustment, the pre-relapse slope was −6.9 (95% CI: −10.6 to −3.2) mL/min/1.73 m2/year and the post-relapse slope was −2.3 (95% CI: −4.6 to 0.1) mL/min/1.73 m2/year. The eGFR slope differed between the relapse and non-relapse groups during the pre-T0 period (P = 0.018), whereas no difference was observed in the post-T0 period (P = 0.10). Conclusion Kidney relapse in AAV occurring after 12 months of treatment initiation was preceded by a decline in eGFR, and was associated with long-term decline in kidney function. Those with no relapse demonstrated a stable eGFR over time. Early identification and treatment of kidney relapses in AAV may improve long-term kidney function.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.008
GPT teacher head0.267
Teacher spread0.259 · 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 routes1
Has abstractyes

Explore more

Same venueNephrology Dialysis TransplantationSame topicVasculitis and related conditionsFrench-language works237,207