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Enregistrement 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 sur OpenAlexaff
Hana Alahmari, Zareen Ahmad, M. Soowamber, Pooneh Akhavan, Mohammad Movahedi, Stephanie Johnson

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Sclerosis and Related Diseases
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineInterstitial lung diseaseRadiographyHigh-resolution computed tomographyRadiologyLungScleroderma (fungus)DiseaseHigh resolutionComputed tomographyPathologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,274
Écart entre enseignants0,249 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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