POS0123 ASSOCIATION OF LUNG IMAGING PATTERN WITH PROGNOSIS AND IMMUNOSUPPRESSION RESPONSE IN CONNECTIVE TISSUE DISEASE ASSOCIATED INTERSTITIAL LUNG DISEASE
Notice bibliographique
Résumé
Background Prognosis in connective tissue disease associated interstitial lung disease (CTD-ILD) is influenced by the underlying diagnosis and chest imaging pattern. Usual interstitial pneumonia (UIP), non-specific interstitial pneumonia (NSIP), and fibrotic hypersensitivity pneumonitis (fHP) patterns can be found across all CTD-ILD subtypes although their impact on disease evolution and treatment response is unclear. Objectives Our goal was to examine the association of lung imaging pattern with CTD-ILD progression, mortality, and immunosuppression response. Methods 615 patients with CTD-ILD enrolled in the Canadian Registry for Pulmonary Fibrosis had high-resolution chest computed tomography (HRCT) from their first ILD clinic visit reviewed in standardized multidisciplinary discussion. All CTD diagnoses were rheumatologist-confirmed. Experienced chest radiologists blinded to clinical data categorized each case into five groups: UIP, NSIP, organizing pneumonia (OP), fHP, and other patterns. Longitudinal percent-predicted forced vital capacity (FVC) and transplant-free survival were compared between imaging groups using linear mixed effects and Cox proportional hazards models adjusted for age, sex, smoking pack-years, and baseline FVC. Linear mixed effects models were used to compare pre- and post-treatment rate of FVC decline in patients with ≥6 months follow-up before and after treatment with mycophenolate, azathioprine, rituximab, cyclophosphamide, and/or tocilizumab. UIP was the reference group for all comparisons. Results The most frequent CTD subtypes were systemic sclerosis (SSc) (33%), rheumatoid arthritis (RA) (20%), and idiopathic inflammatory myopathy (IIM) (16%) with NSIP pattern present in 54% of all CTD-ILD (Table 1). On multivariable analyses among all CTD-ILD patients, NSIP was associated with a slower rate of FVC decline by 1.1%/year (0.2, 1.9) and a lower mortality HR (95%CI) of 0.65 (0.45, 0.93) compared to UIP. OP was also associated with a slower rate of FVC decline by 3.5%/year (2.0, 4.9) and a lower mortality HR (95%CI) of 0.18 (0.05, 0.57) compared to UIP. In contrast, fHP had a higher mortality HR (95%CI) of 1.58 (1.01, 2.40). The rate of FVC decline after treatment was not significantly different compared to pre-treatment in the UIP group but was slower in the NSIP group by 2.1%/year (1.4, 2.8). Subgroup analyses in RA-ILD and SSc-ILD showed the persistence of fHP having a higher mortality compared to UIP in RA-ILD. Conclusion The presence of an NSIP pattern was associated with improved outcomes and immunosuppression response compared to UIP in the overall CTD-ILD group. The findings of fHP associated with worse survival compared to UIP in CTD-ILD and in the RA-ILD are novel. These findings need to be further confirmed in disease specific cohorts and randomized trials of immunosuppression in patients with CTD-ILD. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests Boyang Zheng: None declared, Daniel-Costin Marinescu: None declared, cameron hague: None declared, Nestor Muller: None declared, darra murphy: None declared, Andrew Churg: None declared, Joanne Wright: None declared, Amna Al-Arnawoot: None declared, Ana-Maria Bilawich: None declared, patrick bourgouin: None declared, Gerald Cox: None declared, celine durand: None declared, Tracy Elliot: None declared, Jen Ellis: None declared, Jolene Fisher Consultant of: Boehringer-Ingelheim, AstraZeneca, Derek Fladeland: None declared, Amanda Grant-Orser: None declared, Gillian Goobie Grant/research support from: Boehringer Ingelheim, Zachary Guenther: None declared, Ehsan Haider: None declared, Nathan Hambly Speakers bureau: Boehringer Ingelheim, Grant/research support from: Boehringer Ingelheim, Janssen, Roche, James Huynh: None declared, Kerri Johannson Consultant of: Boehringer-Ingelheim, Hoffman-La Roche Ltd, geoff karjala: None declared, Nasreen Khalil: None declared, Martin Kolb Speakers bureau: Roche, Novartis, Boehringer Ingelheim, Grant/research support from: Boehringer Ingelheim, Pieris, Roche, Jonathon Leipsic Speakers bureau: GE Healthcare, Philips Healthcare, Stacey Lok Speakers bureau: Boehringer Ingelheim, sarah macisaac: None declared, micheal mcinnis: None declared, Helene Manganas Grant/research support from: Boehringer Ingelheim Canada, Hoffmann La Roche, Galapagos, BMS, Veronica Marcoux Grant/research support from: Astra Zeneca,Roche,Boehringer Ingelheim, John Mayo: None declared, julie morisset Speakers bureau: Roche, Boehringer Ingelheim, Ciaran Scallan: None declared, Tony Sedlic: None declared, shane shapera Consultant of: AstraZeneca, Boehringer Ingelheim, Hoffman LaRoche, Kelly Sun: None declared, victoria tan: None declared, Alyson Wong: None declared, Christopher Ryerson Speakers bureau: Boehringer Ingelheim, Hoffmann-La Roche, Astra Zeneca, Consultant of: Boehringer Ingelheim, Hoffmann-La Roche, Astra Zeneca.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».