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Lung Transplantation Outcomes of Patients with Interstitial Pneumonia with Autoimmune Features

2025· article· en· W4411884102 on OpenAlexaffvenueabout
Alec Yu, Hye-In Kim, Robert D. Levy, Jennifer M. Wilson, John Yee, Kun Huang

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLung transplantationInternal medicineTransplantationIdiopathic pulmonary fibrosisPulmonary function testingLungIntensive care medicine

Abstract

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Objectives Interstitial pneumonia with autoimmune features (IPAF) is a designation proposed in 2015 by the European Respiratory Society/American Thoracic Society (ERS/ATS) to describe patients who have interstitial lung disease (ILD) and combinations of clinical, serologic, and/or pulmonary morphologic features of an underlying systemic autoimmune condition, but do not meet rheumatologic criteria for a characterized connective tissue disease (CTD).[1] Previous studies have suggested that patients with IPAF have worse clinical outcomes and survival compared to those with CTD-associated ILD, but better than those with idiopathic pulmonary fibrosis (IPF).[2] Survival after lung transplantation is similar between CTD-associated ILD and IPF.[3] Limited data are available regarding post-lung transplant outcomes in patients with IPAF. The objective of our study is to compare post-transplant survival, lung function, and other complications in IPAF with IPF. Methods We reviewed the data of all patients who underwent lung transplantation in British Columbia, Canada between January 1, 2014, and April 30, 2024. Diagnoses of IPAF were made through a multidisciplinary approach in conjunction with ILD respirologists, chest radiologists, rheumatologists, and pulmonary pathologists. All IPAF cases met the 2015 ERS/ATS criteria and had compatible explant pathology. Continuous variables were assessed with Mann-Whitney U tests and categorical variables with χ2 tests. Results We identified 16 patients with IPAF and compared them to 32 randomly selected patients with IPF. Patient baseline characteristics before lung transplant and post-transplant outcomes are summarized in Table 1. Before transplant, patients with IPAF were more likely to be on immunosuppression (63% vs 6%, p<0.001), and less likely to be on antifibrotics (19% vs 69%, p<0.001). Age, smoking status, sex, Charleson comorbidity index, and pre-transplant forced vital capacity (FVC), diffusing capacity for carbon monoxide (DLCO), and pulmonary artery systolic pressure (PASP) were similar between the 2 groups. Post lung transplant, no difference was seen in survival at 1 year or cumulative survival at the last follow-up. At 1-year follow-up, no difference was seen in FVC and DLCO, rates of acute rejection, or hospitalization for infection. At last follow-up, no difference was seen in the rates of chronic lung allograft dysfunction (CLAD) or post-transplant malignancy. Table 1. Patient baseline characteristics before lung transplant and post-transplant outcome. Conclusion We found no significant difference in post-transplant survival, lung function, infectious or rejection complications in patients who underwent lung transplantation for IPAF compared with IPF at our center. This study is the first of its kind to report both short-term and long-term outcomes of lung transplantation in patients with IPAF. [1.] Fischer A. Eur Respir J 2015;46:976-87. [2.] Dai J. Clin Rheumatol 2018;37:2125-32. [3.] Zhang N. Arthritis Care Res (Hoboken) 2023;75:2389-98. Best Abstract by a Rheumatology Resident Award

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)0.002
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.003
GPT teacher head0.230
Teacher spread0.227 · 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 routes3
Has abstractyes

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