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Record W4411420903 · doi:10.1016/j.ard.2025.05.845

POS0463 High-Resolution Computerized Tomography Chest Radiographic Patterns and their Impact on Systemic Sclerosis-associated Interstitial Lung Disease Survival

2025· article· en· W4411420903 on OpenAlexaff
Hana Alahmari, Zareen Ahmad, M. Soowamber, Pooneh Akhavan, Mohammad Movahedi, Stephanie Johnson

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineInterstitial lung diseaseRadiographyHigh-resolution computed tomographyRadiologyLungScleroderma (fungus)DiseaseHigh resolutionComputed tomographyPathologyInternal medicine

Abstract

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Background: SSc-associated interstitial lung disease (SSc-ILD) occurs in more than half of SSc patients and is characterized by bilateral parenchymal fibrosis and inflammatory changes [1]. In 2001, the American Thoracic Society (ATS) and European Respiratory Society (ERS) published an approach to the standardized classification of ILD; Idiopathic interstitial pneumonia includes idiopathic pulmonary fibrosis (IPF), usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia (COP), acute interstitial pneumonia (AIP), respiratory bronchiolitis-associated interstitial lung disease (RB-ILD), desquamative interstitial pneumonia (DIP), and lymphocytic interstitial pneumonia (LIP). The interstitial inflammation/fibrosis pattern by HRCT was not a predictive variable for progression or survival in early SSc studies [3]. Recent data, however, shows mixed conclusions on the prognostic outcome, with some studies showing no prognostic value of a UIP pattern compared with NSIP and others showing worse outcomes in patients with a UIP pattern on HRCT compared to NSIP [4]. Our study aims to understand better the epidemiology and the role of HRCT chest patterns in SSc-ILD and to compare survival between the most common radiological patterns of ILD on HRCT, in people with SSc-ILD. Objectives: To assess survival stratified by interstitial lung disease (ILD) patterns in a large cohort of unselected systemic sclerosis (SSc) patients and to explore predictors of survival. Methods: We conducted a cohort study of SSc-ILD patients at a multi-clinic tertiary referral center. Baseline HRCT chest reports were reviewed. The radiologists reported an ATS/ERS radiographic pattern of UIP, NSIP, RP-ILD, DIP, LIP, COP, or other or not reported. The primary outcome was time to all-cause mortality. The survival status of subjects lost to follow-up was systematically tracked through the family physician, referring physician, and/or online obituary databases. Kaplan-Meier survival curves and Cox proportional hazards compared survival between SSc-ILD patients with UIP and NSIP patterns. Results: Four hundred and eleven SSc patients were included in this study. The UIP pattern (61.1%) was the most frequent, followed by NSIP (34.6%), COP (1.2%), LIP (1.2%), and DIP (0.7%), while none had RB-ILD. Compared to NSIP, patients with UIP were significantly more likely to have digital ulcers (RR 1.19, 95%CI 1.03-1.38) and pulmonary arterial hypertension (RR 1.33, 95%CI 1.15-1.53). Of 393 patients with UIP and NSIP patterns, there were 136 (34.6%) deaths. Five-year survival in both groups was 87%, but a marked decline occurred in the long term. The probability of 15-year survival was significantly worse in those with the UIP pattern (54.5% (95%CI 46.4-61.8%) compared to the NSIP pattern (76.2% (95%CI 65.2-84.1%), Kaplan Meier survival curves log-rank test p=0.008. Although UIP (HR 1.77 (95%CI 1.15- 2.70) was associated with worse survival on univariate analysis, this association was attenuated and nonsignificant (HR 1.67 (95%0.87- 3.23) on multivariate analysis. Older age at onset, pulmonary hypertension, and scleroderma renal crisis were independently associated with worse survival in UIP compared to NSIP groups. Conclusion: In this study, UIP was the most frequent HRCT ILD pattern associated with increased vasculopathy manifestations of pulmonary arterial hypertension and digital ulceration. However, this pattern does not independently confer an increased risk of mortality. Figure 1High-resolution computerized tomography chest radiographic patterns in SSc-ILD. REFERENCES: [1] Denton CP, Wells AU, Coghlan JG. Major lung complications of systemic sclerosis. Nature reviews Rheumatology 2018;14:511-27. [2] Bouros D, Wells AU, Nicholson AG, et al. Histopathologic subsets of fibrosing alveolitis in patients with systemic sclerosis and their relationship to outcome. Am J Respir Crit Care Med 2002;165:1581-6. [3] Takei R, Arita M, Kumagai S, et al. Radiographic fibrosis score predicts survival in systemic sclerosis-associated interstitial lung disease. Respirology 2018;23:385-91. [4] Mango RL, Matteson EL, Crowson CS, Ryu JH, Makol A. Assessing Mortality Models in Systemic Sclerosis-Related Interstitial Lung Disease. Lung 2018;196:409-16. Table 1Univariable and multivariable Cox regression models for the association between ILD subtype and all-cause mortality.Univariable analysisMultivariable analysisHRs (95% CI), p-valueHRs (95% CI), p-valueUIP (Ref= NSIP)1.77 (1.15- 2.70), 0.0091.67 (0.87- 3.23), 0.08Age1.06 (1.04, 1.07), <0.00011.06 (1.04, 1.09), <0.0001Male1.44 (0.99-2.11), 0.0571.72 (0.84, 3.50), 0.136White ethnicity2.18 (1.20-3.94), 0.011.39 (0.73, 2.63), 0.312Pulmonary arterial hypertension2.05 (1.45-2.90), <0.00012.04 (1.11, 3.74), 0.022Scleroderma renal crisis2.24 (1.33-3.78), 0.0035.43 (2.32, 12.7), <0.0001Lung cancer2.06 (0.65-6.51), 0.2166.10 (0.70, 53.1), 0.101 Bold denotes statistical significance . Acknowledgements: NIL . 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)0.001
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 score0.004
Threshold uncertainty score0.014

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.274
Teacher spread0.249 · 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 routes1
Has abstractyes

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