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Record W4411395318 · doi:10.1016/j.ard.2025.06.891

ABS0847 RETROSPECTIVE SINGLE-CENTRE ANALYSIS OF SURVIVAL AND SAFETY SIGNALS IN AUTOIMMUNE RHEUMATIC DISEASE PATIENTS UNDERGOING LUNG TRANSPLANTATION

2025· article· en· W4411395318 on OpenAlexaff
Faye A. H. Cooles, A Obrzut, Na Lu, R. Levy, J. Antonio Aviña‐Zubieta, Diane Lacaille

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsVancouver General HospitalResearch CanadaUniversity of British Columbia
Fundersnot available
KeywordsMedicineRheumatic diseaseLung transplantationTransplantationRetrospective cohort studyInterstitial lung diseaseAutoimmune diseaseInternal medicineDiseaseLung

Abstract

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Background: Lung transplant is increasingly offered for severe cardiopulmonary disease in autoimmune rheumatic diseases (ARDs). Patients frequently have extrapulmonary manifestations requiring immunosuppression, but how this affects clinical trajectory post-transplant is unclear. Objectives: Our study objectives were to compare complications post-transplant in people with and without ARDs; and in people with ARDs with and without transplant. Methods: All lung/heart-and-lung transplants performed in British Columbia from 01/01/1990 to 01/01/2022 were identified using administrative health data. Transplants with ARD were matched ≥1:10 (propensity score matching with replacement) on age, sex, transplant-year, previous disease modifying anti-rheumatic drugs (DMARD)/biologic use, socioeconomic status, and ARD, generating a) ARD-controls and b) transplant-controls. Mortality, major adverse cardiovascular events (MACE) and infections were compared and DMARD use post-transplant quantified. Statistical modelling applied multivariable Cox proportional and Fine-Gray sub-distribution hazards and Poisson regression. Results: Cohorts comprised n=85 ARD-transplants; n=42,062 ARD-controls; and n=3,412 transplant-controls. ARD-transplant patients had increased mortality compared with transplant-controls and ARD controls (IR 112.6 vs 58.6 and 11.3 respectively). However, in multivariable regression analyses, risk of mortality, MACE, and infection were comparable between transplant cohorts, but were higher in ARD-transplant vs. ARD-controls. Rheumatoid arthritis (RA)-transplants had higher risk of MACE than RA-controls (HR 5.07 95% CI 1.67, 15.41) which was not seen for other ARDs. 90% of ARD-transplant remained on pre-transplant DMARDS. Those stopping DMARDs had lower infection risk (HR 0.25 95% CI 0.14, 0.44) but paradoxically increased mortality (HR 10.87 95% CI 3.89, 30.44). Conclusion: Reassuringly, when adjusting for comorbidities and patient characteristics, lung transplants with and without concurrent ARD had similar mortality, MACE, and infection risk. Future multi-centre studies are needed to better understand the impact of DMARD class post-transplant. REFERENCES: NIL . Acknowledgements: We would like to acknowledge John Esdaile for his support and guidance. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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

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