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Record W4411416050 · doi:10.1016/j.ard.2025.06.062

POS0701 LUNG TRANSPLANTATION OUTCOMES IN PATIENTS WITH INTERSTITIAL PNEUMONIA WITH AUTOIMMUNE FEATURES

2025· article· en· W4411416050 on OpenAlexaffabout
Cheuk‐Man Yu, H. J. Kim, Robert D. Levy, Juliana Wilson, John Yee, Kun Huang

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsArthritis Research Centre of CanadaResearch CanadaUniversity of British Columbia
Fundersnot available
KeywordsMedicineLung transplantationPneumoniaLungTransplantationInterstitial pneumoniaImmunologyIntensive care medicineInternal medicine

Abstract

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Background: 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. Objectives: 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 the Mann-Whitney U test and categorical variables with Fisher's Exact test. Results: We identified 20 patients with IPAF and compared them to 64 patients with IPF who underwent double lung transplantation during the same period (Table 1). IPAF patients more likely to be female (50% vs 17%, p=0.006). Before transplant, patients with IPAF were more likely to be on immunosuppression (60% vs 6%, p<0.001), and less likely to be on antifibrotics (20% vs 64%, 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 two groups. The median duration of post-lung transplant follow-up was 3.6 years in the IPAF group and 3.8 years in the IPF group. No significant 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. 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 centre. This study is the first of its kind to report both short-term and long-term outcomes of lung transplantation in patients with IPAF. REFERENCES: [1] Fischer A, Antoniou KM, Brown KK, Cadranel J, Corte TJ, du Bois RM, et al. An official european respiratory society/american thoracic society research statement: Interstitial pneumonia with autoimmune features. Eur Respir J 2015;46:976-87. [2] Dai J, Wang L, Yan X, Li H, Zhou K, He J, et al. Clinical features, risk factors, and outcomes of patients with interstitial pneumonia with autoimmune features: A population-based study. Clin Rheumatol 2018;37:2125-32. [3] Zhang N, Liu S, Zhang Z, Liu Y, Mi L, Xu K. Lung transplantation: A viable option for connective tissue disease? Arthritis Care Res (Hoboken) 2023;75:2389-98. Table 1Summary of Pre- and Post-transplant Data.VariablesIPF (n=64)IPAF (n=20)p-valueDemographicsAge at ILD Diagnosis, median [IQR]60.5 [58 – 64]58 [53 - 62]0.08Age at Transplant, mean [IQR]65 [62 – 68]63 [59 - 67]0.31Sex (women), n (%)11 (17)10 (50)0.006Race (White), n (%)51 (80)16 (80)1.0Smoker, n (%)47 (73)13 (65)0.57BMI, median [IQR]27.6 [24 – 29.2]28.6 [22 – 30]0.65Charleson Comorbidity index, median [IQR]3 [2 - 4]3 [2 – 3]0.88Variables at TransplantFVC (%), median [IQR]51 [39 – 64]49 [37 – 55]0.23DLCO (%), median [IQR]31 [24 – 38]27 [23 – 30]0.06PASP (mmHg), median [IQR]35 [31 – 42]35 [28 – 39]0.90Diagnosis to Transplant (years), median [IQR]3.9 [2.3 – 5.8]5.2 [2.8 – 5.9]0.31Immunosuppression*, n (%)4 (6)12 (60)< 0.001Antifibrotic, n (%)41 (64)4 (20)< 0.001ICU admission prior to transplant, n (%)9 (14)5 (25)0.31CMV mismatch Donor (+)/ Recipient (-), n (%)9 (14)6 (30)0.18Dominant Explant PathologyUIP, n (%)62 (97)7 (35)< 0.001NSIP, n (%)0 (0)8 (40)< 0.001Other, n (%)0 (0)1 (5)0.24Unclassifiable, n (%)2 (3)4 (20)0.031 Year OutcomesAlive, n (%)59 (92)18 (90)0.67Post-transplant ICU days, median [IQR]4 [3 – 9]3.5 [2 – 6.5]0.16Post-transplant hospitalization days, median [IQR]22 [17 – 31]22 [16 – 26]0.42FVC (%), median [IQR]84 [67 – 99]80 [68 – 99]0.69DLCO (%), median [IQR]61 [45 – 75]64 [56 – 72]0.59Acute Rejection, n (%)31 (48)9 (45)0.80Pneumonia requiring hospitalization, n (%)20 (31)5 (25)0.78Non-pulmonary infection requiring hospitalization, n (%)14 (22)6 (30)0.54Fungal infection requiring therapy, n (%)22 (34)5 (25)0.59CMV viremia requiring therapy, n (%)15 (23)6 (30)0.56Long-term OutcomesLength of follow-up (years), median [IQR]3.6 [2.1 – 6.2]3.8 [1.5 – 7.3]0.98Alive at last follow-up**, n (%)42 (66)17 (85)0.16CLAD**, n (%)18 (28)6 (30)0.98Post-transplant malignancy**, n (%)16 (25)4 (20)0.97All continuous variables analyzed with the Mann-Whitney U testAll categorical variables analyzed with the Fisher's Exact test*Does not include prednisone/ methylprednisone** Analyzed with Kaplan Meier survival or incidence analysis Acknowledgements: The authors express their gratitude to BC Transplant and all of the members of the Vancouver General Hospital Lung Transplant team. 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.

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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)0.001
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.009
GPT teacher head0.301
Teacher spread0.292 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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