OP0319 LUNG TRANSPLANT OUTCOMES IN PATIENTS WITH MYOSITIS- AND SYSTEMIC SCLEROSIS-ASSOCIATED INTERSTITIAL LUNG DISEASE COMPARED TO IDIOPATHIC PULMONARY FIBROSIS: A MULTICENTRIC RETROSPECTIVE ANALYSIS
Bibliographic record
Abstract
Background: Interstitial lung disease (ILD) is a frequent complication of idiopathic inflammatory myositis (IIM) and systemic sclerosis (SSc), associated with significant morbidity and mortality. Pulmonary manifestations range from subclinical ILD to rapidly progressive respiratory failure. ILD affects 40–50% of myositis patients, often presenting as part of antisynthetase syndrome (ASyS) or anti-melanoma differentiation-associated protein 5 (MDA5) dermatomyositis, and up to 60% of SSc patients, particularly those with anti-Scl70 antibodies. Despite advances in immunosuppressive and antifibrotic agents, many progress to end-stage respiratory failure and may require lung transplantation. Unlike idiopathic pulmonary fibrosis (IPF), data on lung transplant outcomes in patients with IIM and SSc remain limited. Informing on the outcomes and challenges of lung transplantation in this specific patient population is critical for tailoring strategies and ultimately improve patients' survival and quality of life. Objectives: The aim of this study was to compare the clinical characteristics and post-lung transplant outcomes in patients with IIM and SSc to those with IPF. Methods: We retrospectively analyzed clinical data collected in patients with ILD who underwent lung transplant between January 1, 2014 and April 30, 2024 at the British Columbia (BC) lung transplant center and since January 1, 2012 at the Centre hospitalier de l'Université de Montréal ILD clinic. Patients with IIM and SSc according to the 2017 and 2013 ACR/EULAR classification criteria were included. Additionally, IPF controls from the BC cohort meeting the 2022 Official ATS/ERS/JRS/ALAT Clinical Practice Guideline were also included. Univariant analyses were performed for continuous and categorical data. Continuous variables were analyzed using the Kruskal-Wallis test, followed by post-hoc pairwise comparisons with Dunn's test for significant results. Categorical variables were assessed using the Chi-squared test, with pairwise comparisons conducted using Fisher's Exact test and Bonferroni correction for multiple comparisons. Statistical significance was defined as p < 0.05. Results: A total of 18 patients with IIM, 23 with SSc, and 64 with IPF were included. Characteristics of patients at baseline before lung transplant, and short- and long-term outcomes following the procedure are presented in Table 1 and 2 respectively. Median post-lung transplantation follow-up durations were 3.5 years (IQR 1.8-5.8) for IIM, 2.1 years (IQR 1.0-5.1) for SSc, and 3.5 years (IQR 2.1-6.2) for IPF. In the 18 IIM patients, 50% was identified as anti-MDA5 dermatomyositis, 39% as anti-synthetase syndrome; 50% had anti-Ro52. In the 23 SSc patients, anti-Scl-70 was found in 35%, and anti-centromere in 12%. Patients with IIM and SSc were younger at the time of ILD diagnosis and lung transplantation, more likely to be female, less likely to have a smoking history, and had fewer comorbidities compared to those with IPF. Patients with SSc had significantly lower diffusing capacity for carbon monoxide (DLCO) and a longer ILD disease duration before lung transplant compared to IIM and IPF patients. IIM and SSc patients were more exposed to immunosuppressants, whereas IPF patients received more antifibrotics. Additionally, IIM patients more frequently required intensive care unit (ICU) and emergency transplantation. In almost all IPF cases, usual interstitial pneumonia (UIP) was the dominant pathology identified on explant. Nonspecific interstitial pneumonia (NSIP) was more frequently observed in SSc and IIM, with organizing pneumonia (OP) and mixed NSIP/OP only seen in IIM cases. Post-transplant, there was no significant difference in 1-year survival or cumulative survival at the last follow-up among the three groups. However, IIM patients required significantly longer post-transplant ICU and hospital care compared to those with SSc and IPF. At the 1-year follow-up, forced vital capacity (FVC) was significantly lower in SSc and IIM patients compared to IPF. However, no significant differences were observed in DLCO, rates of acute rejection, or hospitalizations due to infections. At the last follow-up, the rates of chronic lung allograft dysfunction (CLAD) or post-transplant malignancies were comparable between the groups. Conclusion: This study highlights key differences in baseline characteristics, explant pathology, and post-transplant outcomes in myositis and SSc in comparison to IPF. While survival, risk of acute and chronic rejection, infections, and post-transplant malignancies were comparable across the three groups, distinctive differences were observed. Patients with myositis were more likely to have rapidly progressive ILD, leading to increased ICU care before transplantation, a higher likelihood of emergency transplantation, and prolonged ICU and hospital stays post-transplant. Furthermore, myositis and SSc were associated with unique explant pathology and lower FVC at 1-year follow-up compared to IPF. These findings emphasize the need for tailored management strategies to optimize outcomes in these distinct patient populations. This study is the first to report short-term and long-term lung transplant outcomes in patients with myositis and SSc, compared to the more commonly studied IPF. REFERENCES: NIL . Acknowledgements: Drs. Chang, Saleh and Yu contributed equally to this manuscript. Drs. Kim and Huang contributed equally to this manuscript. We thank the support from Fresenius Kabi and Pfizer in this investigator initiated clinical research project. Disclosure of Interests: Navid Saleh: None declared, Angela Chang: None declared, Alec Yu: None declared, Darya Seyed-Jalaledin: None declared, Sabrina Hoa: None declared, Robert Levy: None declared, Jennifer Wilson: None declared, Charles Poirier: None declared, John Yee: None declared, James Choi: None declared, Océane Landon-Cardinal: None declared, Hyein Kim: None declared, Kun Huang Fresenius Kabi, Novartis, Abbvie, I received $10,000 canadian dollars from Fresenius Kabi and Pfizer each for investigator initiated research projects. © 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".