P167 Guselkumab and IL-17 inhibitors show comparable treatment persistence and effectiveness in psoriatic arthritis outcomes: 6-month interim results of the PsABIOnd observational cohort study
Notice bibliographique
Résumé
Abstract Background/Aims Many drugs have demonstrated efficacy in PsA through randomized controlled trials (RCTs); however, real-world long-term data of drugs are scarce. PsABIOnd is a large, ongoing, global observational study in PsA. The aim of this interim analysis (IA) of the first ≥600 participants (pts) enrolled out of 1300 planned pts in PsABIOnd was to assess treatment persistence and achievement of clinical PsA outcomes at 6 months (M). Methods PsABIOnd (NCT05049798) is an international, prospective, observational study in PsA pts starting guselkumab (GUS) or interleukin-17 inhibitors (IL-17i) as 1st-to-4th line of biologic therapy (monotherapy or in combination with other agents) per standard of care. The primary outcome is treatment persistence at 36M [1]. In this IA, the subset of pts enrolled in the PsABIOnd study who had an assessment at the 6M visit (+/-3M) were analysed according to their initial treatment arm, regardless of later switches. Persistence on treatment was assessed over 6M by treatment line via Kaplan-Meier estimator function. Propensity score (PS) analysis was used to evaluate hazard ratio of stopping or switching GUS vs IL-17i prior to the 6M visit, adjusting for baseline (BL) variables imbalances across cohorts. Effectiveness was assessed at the 6M visit (descriptive unadjusted reports) by treatment line and included rates of achievement of Low Disease Activity (LDA)/remission (REM) by cDAPSA and DAPSA (among pts with polyarticular PsA at BL), MDA (among non-MDA achievers at BL), psoriasis BSA<3% (among pts with BSA≥3% at BL), and resolution of enthesitis by Leeds Enthesitis Index (LEI; among pts with LEI≥1 at BL) and dactylitis (among pts with dactylitis at BL). Results As of 08-Jan-2024, a total of 360 and 326 pts receiving GUS or IL-17i, respectively, as their initial treatment had follow-up data at the 6M visit: mean (GUS/IL-17i) age at BL was 52.0/53.6 years, and 63.1/63.8% pts had previously received at least one targeted drug. The persistence on treatment at the 6M visit was high in both cohorts, with 339/360 (94.2%) pts in the GUS group and 304/326 (93.3%) pts remaining on their initial treatment line at this time point (PS-adjusted hazard ratio of GUS vs IL-17i stop/switch [95% confidence interval]: 0.87 [0.47-1.61]). Reasons for initial treatment line discontinuation were comparable between groups. Treatment effectiveness was similar for GUS vs IL-17i at the 6M visit, with similar rates of achievement cDAPSA LDA/REM (39.7% [95% confidence intervals (CI): [32.3-47.3] vs 34.3% [27.3-41.7]; DAPSA LDA/REM (38.0% [30.0-46.5] vs 38.9% [31.1-47.2]); BSA<3% (34.9% [29.7-40.1] vs 31.5% [26.1-36.9]); MDA (24.0% [19.4-29.0] vs 28.6% [23.4-34.2]); LEI resolution (45.1% [37.6-52.8] vs 48.5% [40.8-56.3]); and dactylitis resolution (60.3% [47.2-72.4] vs 65.6% [52.7-77.1]). Conclusion PsA pts had similar persistence on treatment with GUS or IL17i, and comparable rates of effectiveness across various PsA domains at 6M. Disclosure S. Siebert: Honoraria; AbbVie, Amgen, AstraZeneca, Janssen, Teijin Pharma. Grants/research support; Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Janssen, UCB. M. Sharaf: Corporate appointments; Employee of EMEA Medical Affairs, Johnson & Johnson Middle East FZ LLC, Dubai United Arab Emirates. Shareholder/stock ownership; Owns stock in Johnson & Johnson. X. Baraliakos: Consultancies; AbbVie, Chugai, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche, UCB. Member of speakers’ bureau; AbbVie, Chugai, Eli Lilly, Janssen, MSD, Novartis, Pfizer, Roche, UCB. Grants/research support; AbbVie, Eli Lilly, Janssen, MSD, Novartis. M. Kishimoto: Consultancies; AbbVie, Amgen, Asahi-Kasei Pharma, Astellas, Ayumi, Bristol Myers Squibb, Chugai, Daiichi-Sankyo, Eisai, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, Tanabe-Mitsubishi, UCB. E. Lubrano: Honoraria; AbbVie, Amgen, Eli Lilly, GlaxoSmithKline, Janssen, Novartis, UCB. P. Rahman: Consultancies; AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, UCB. Grants/research support; Janssen, Novartis. Other; Janssen. E. Rampakakis: Corporate appointments; Employee of JSS Medical Research. Consultancies; Janssen. L. Köleséri: Corporate appointments; Employee of IQVIA. Consultancies; Janssen. M. Koivunen: Corporate appointments; Employee of EMEA Medical Affairs, Janssen-Cilag Oy, a Johnson & Johnson company. Shareholder/stock ownership; Owns stock in Johnson & Johnson. F. Lavie: Corporate appointments; Employee of Immunology Global Medical Affairs, Janssen Pharmaceutical Companies. Shareholder/stock ownership; Owns stock in Johnson & Johnson. E. Soriano: Consultancies; AbbVie, Janssen, Novartis, Roche. Member of speakers’ bureau; AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Janssen, Novartis, Pfizer, Roche, UCB. Grants/research support; AbbVie, Janssen, Novartis, Pfizer, Roche, UCB. R. Queiro: Consultancies; AbbVie, Amgen, Celgene, Janssen, Eli Lilly, MSD, Novartis, Pfizer. Grants/research support; AbbVie, Janssen, Novartis. F. Behrens: Consultancies; AbbVie, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Chugai, Eli Lilly, Galapagos, Genzyme, Gilead, Janssen, MSD, Novartis, Pfizer, Roche, Sanofi, UCB. Grants/research support; Celgene, Chugai, Janssen, Pfizer, Roche.
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 | 0,010 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
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 ».