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Enregistrement W4411411712 · doi:10.1016/j.ard.2025.05.683

POS0296 MYOCARDIAL INFARCTION, STROKE, VENOUS THROMBOEMBOLIC EVENTS, SERIOUS INFECTIONS AND MALIGNANCY IN PATIENTS WITH PSORIATIC ARTHRITIS TREATED WITH TOFACITINIB COMPARED TO BIOLOGIC TREATMENTS IN THE UNITED STATES

2025· article· en· W4411411712 sur OpenAlexaff
Marina Magrey, M.A. Gianfrancesco, L. Fallon, A. Yndestad, Ivona Vranić, David Gruben, J R Curtis

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueRheumatoid Arthritis Research and Therapies
Établissements canadiensPfizer (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineTofacitinibPsoriatic arthritisMyocardial infarctionMalignancyStroke (engine)DermatologyArthritisInternal medicineRheumatoid arthritisSurgeryCardiology

Résumé

récupéré en direct d'OpenAlex

Background: There is limited real-world safety information in patients (pts) with psoriatic arthritis (PsA) treated with tofacitinib vs biologic treatments. Objectives: This observational study examined the risk of inpatient diagnoses of myocardial infarction (MI)/stroke, venous thromboembolic events (VTE), serious infections and malignancy (excluding non-melanoma skin cancer) among PsA pts initiating tofacitinib or biologics, from an adjudicated United States (US) closed medical/pharmacy claims database (Komodo Health). Methods: Pts with PsA aged ≥18 years newly initiating tofacitinib or a biologic (adalimumab, certolizumab pegol, etanercept, golimumab, infliximab [tumour necrosis factor inhibitors (TNFi)]; secukinumab/ixekizumab [interleukin-17 inhibitors (IL-17i)]; risankizumab or ustekinumab) from 15 December 2017–30 September 2022, with ≥12 months of continuous enrolment prior to the index date (date of PsA therapy initiation) were included. New use=no prior use during the baseline period (pts with Janus kinase inhibitor use any time prior to index date were excluded). A pt could be a new user once for each specific drug, but could also be a new user for a second drug class. Stabilised inverse probability treatment weights (sIPTW) were calculated using 17 covariates (demographics/treatment history/comorbidities) in the main analysis, and an additional 53 in a sensitivity analysis to control for additional comorbidities/PsA-related measures/healthcare utilisation variables. Other sensitivity analyses examined the proportion of pts with unadjusted VTE risk factors pre/post-index date. Crude incidence rates (IRs) per 100 pt-years (PYs) were calculated. Cox proportional hazards models with sIPTW were used to calculate adjusted hazard ratios (aHRs) with bootstrapping to calculate 95% confidence intervals (CIs). Results: In total, 48,167 pts were included (tofacitinib, N=3,166; TNFi, N=26,760; IL-17i, N=20,252; risankizumab, N=4,381; ustekinumab, N=4,499) (Table 1). Mean age (all treatments) at index date ranged from 48.2–50.3 years, and mean follow-up was 288.5–347.0 days. At baseline, pts initiating tofacitinib were less likely to have a psoriasis diagnosis vs biologics (60.7% vs 70.9–94.8%). More pts initiating tofacitinib had used ≥3 prior biologics or other advanced treatments (23.7%) vs TNFi (5.4%), IL-17i (8.5%), risankizumab (15.5%) or ustekinumab (12.0%). Baseline systemic corticosteroid use was highest in tofacitinib users (20.8%) vs those using biologics (8.3–16.0%). Pts initiating tofacitinib were less likely to have history of MI/stroke or VTE (6.5–6.6%) vs biologics (7.2–9.4%), or history of malignancy (4.2%) vs biologics (4.5–5.3%) except TNFi (3.0%). Crude IRs per 100 PYs among treatments ranged from 0.27–0.61 for MI/stroke, 0.17–0.42 for VTE, 1.78–2.53 for serious infections and 0.74–1.06 for malignancy (Figure 1). In the main analysis, there were no statistically significant differences in risk of developing MI/stroke, serious infections or malignancy between treatments (Figure 1). There was a significantly decreased risk of VTE with TNFi vs tofacitinib (aHR 0.26 [95% CI 0.14, 0.62]), but not with other biologics. Sensitivity analyses were consistent with the main findings including that more pts on tofacitinib had surgery during the baseline period (45.9–46.7%) and 6 months after the index date (28.1–28.9%) vs biologics (40.2–44.3% and 22.2–27.4%, respectively). Conclusion: In this US claims dataset, there were no significant differences in risk of developing MI/stroke, serious infections or malignancy for tofacitinib vs biologics, among PsA pts not enriched for cardiovascular/VTE risk factors. A decreased risk of VTE with TNFi vs tofacitinib was found, aligning with previous clinical trial data. Limitations included the variable numbers of safety events across treatments and potential uncontrolled confounding of VTE-specific risk factors, such as surgery. 3 REFERENCES: NIL . Acknowledgements: This study was sponsored by Pfizer. Medical writing support, under the direction of the authors, was provided by Kimberley Haines, MSc, CMC Connect, a division of IPG Health Medical Communications, and was funded by Pfizer, New York, NY, USA, in accordance with Good Publication Practice (GPP 2022) guidelines (Ann Intern Med 2022; 175: 1298–1304). Disclosure of Interests: Marina Magrey Consultant: AbbVie, Eli Lilly, Johnson & Johnson, Novartis, Pfizer Inc, UCB, Grant/research support: Amgen, BMS, Milena A Gianfrancesco Shareholder: Pfizer Inc, Employee: Pfizer Inc, Lara Fallon Shareholder: Pfizer Inc, Employee: Pfizer Inc, Arne Yndestad Shareholder: Pfizer Inc, Employee: Pfizer Inc, Ivana Vranic Shareholder: Pfizer Inc, Employee: Pfizer Inc, You-Li Ling Shareholder: Pfizer Inc, Employee: Pfizer Inc, David C Gruben Shareholder: Pfizer Inc, Employee: Pfizer Inc, Jeffrey R Curtis Consultant: Pfizer Inc, Grant/research support: Pfizer Inc. © 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)0,003
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 enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,013
Tête enseignante GPT0,273
Écart entre enseignants0,261 · 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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