PTH-067 Incidence of pneumonia and other respiratory tract infections with vedolizumab treatment: clinical trial experience
Notice bibliographique
Résumé
Introduction Vedolizumab (VDZ) binds to α4β7 integrin and selectively blocks gut-specific lymphocyte trafficking. Selectivity could reduce the risk of respiratory tract infections (RTIs) compared with therapies causing systemic immunosuppression (e.g. anti-tumour necrosis factor-alpha [TNFα] agents). The annual pneumonia rate in inflammatory bowel disease is higher than in healthy individuals (13.8/1000 vs 7.6/1000; incident rate ratio 1.82; 95% CI: 1.75–1.88) and the risk is increased with anti-TNFα treatment (odds ratio 1.28; 95% CI: 1.08–1.52).1 We report rates of RTIs associated with VDZ treatment in clinical trials. Method Data from GEMINI 1 (ulcerative colitis; UC) and GEMINI 2 (Crohn’s disease; CD) and an ongoing open-label extension (OLE; data cut-off: May 2015; UC and CD) were used to calculate rates of lower RTIs (LRTIs) and upper RTIs (URTIs). A Cox proportional hazards model was used to identify potential predictors of these adverse events (AEs) in a pooled analysis of GEMINI 1/2. Results In GEMINI 1/2 (n=1434, VDZ; n=297, placebo) and in the OLE (n=2243, all VDZ), mean (SD) ages were 37.9 (12.7) years and 39.1 (13.2) years; 47.8% and 49.7% were female sex; 17.2% and 18.1% were current smokers and 27.3% and 26.3% were former smokers, respectively. In GEMINI 1/2, exposure-adjusted incidence rates per 100 patient-years [IRs] of LRTIs were similar in VDZ and placebo groups: LRTIs overall 7.7 vs 8.5; pneumonia 1.0 vs 1.2; bronchopneumonia <0.1 vs 0.6 and primary atypical pneumonia <0.1 vs 0, respectively. The IR of URTIs overall was higher with VDZ (38.7) than with placebo (33.0). OLE IRs were no greater than for GEMINI 1/2: LRTIs overall 5.0; URTIs overall 23.5. Most RTIs were not serious. Two LRTIs (VDZ, GEMINI 2) and two URTIs (one in OLE; one in GEMINI 2) resulted in discontinuation. Predictors of increased LRTI incidence were: current/former smoker (hazard ratio [HR] 1.97; 95% CI: 1.17–3.31; p=0.0110 and 1.74; 95% CI 1.06–2.87, p=0.0291, respectively), female sex (HR 1.84; 95% CI: 1.19–2.83; p=0.0058) and prior anti-TNFα use (HR 1.70; 95% CI: 1.06–2.73; p=0.0281). Predictors of URTIs were: current smoker (HR: 1.35; 95% CI: 1.04–1.75; p=0.0225), concomitant narcotic use (HR: 1.30; 95% CI: 1.04–1.64; p=0.0215) and prior anti-TNFα use (HR: 1.50; 95% CI: 1.20–1.88; p=0.0004). Conclusion In this post hoc analysis, VDZ treatment of UC/CD was not associated with an increased incidence of LRTIs, including pneumonia, compared with placebo. W.Palo is now at Abbvie Inc., North Chicago, IL, USA Reference . Long MD, et al. Am J Gastroenterol2013;108:240 Disclosure of Interest B. Feagan Conflict with: Abbott/AbbVie, Amgen, Astra Zeneca, Bristol-Myers Squibb (BMS), Janssen Biotech (Centocor), JnJ/Janssen, Roche/Genentech, Millennium, Pfizer, Receptos, Santarus, Sanofi, Tillotts, UCB Pharma, Conflict with: Abbott/AbbVie, ActoGeniX, Akros, Albireo Pharma, Amgen, Astra Zeneca, Avaxia Biologics Inc., Avir Pharma, Axcan, Baxter Healthcare Corp., Biogen Idec, Boehringer-Ingelheim, Bristol-Myers Squibb, Calypso Biotech, Celgene, Elan/Biogen, enGene, Ferring Pharma, Roche/Genentech, gICare Pharma, Gilead, Given Imaging Inc., GSK, Ironwood Pharma, Janssen Biotech (Centocor), JnJ/Janssen, Kyowa Hakko Kirin Co Ltd., Lexicon, Lilly, Lycera BioTech, Merck, Mesoblast Pharma, Millennium, Nektar, Nestles, Novo Nordisk, Pfizer, Prometheus Therapeutics and Diagnostics, Protagonist, Receptos, Salix Pharma, Serono, Shire, Sigmoid Pharma, Synergy Pharma Inc., Takeda, Teva Pharma, TiGenix, Tillotts, UCB Pharma, Vertex Pharma, VHsquared Ltd., Warner Chilcott, Wyeth, Zealand, Zyngenia, Conflict with: director of Robarts Clinical Trials; membership (scientific advisory board) of Abbott/AbbVie, Amgen, Astra Zeneca, Avaxia Biologics Inc., Bristol-Myers Squibb, Celgene, Centocor Inc., Elan/Biogen, Ferring, JnJ/Janssen, Merck, Nestles, Novartis, Novo Nordisk, Pfizer, Prometheus Laboratories, Protagonist, Salix Pharma, Takeda, Teva, TiGenix, Tillotts Pharma AG, UCB Pharma, F Bhayat Conflict with: Takeda Development Centre Europe Ltd, J Khalid Conflict with: Takeda Development Centre Europe Ltd, W Palo Conflict with: Takeda Development Centre Americas, Inc.; Abbvie, Inc., A Blake Conflict with: Takeda Development Centre Europe Ltd, M Shetzline Conflict with: Takeda Pharmaceuticals International Co., S Travis Conflict with: AbbVie, IOIBD, Lilly, UCB, Vifor, and Norman Collison Foundation, Conflict with: AbbVie, Amgen, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Chemocentryx, Cosmo, Ferring, Giuliani SpA, GlaxoSmithKline, Janssen, Lilly, MSD, Neovacs, NovoNordisk, Norman Collison Foundation, Novartis, NPS Pharmaceuticals, Pfizer, Proximagen, Receptos, Shire, Sigmoid Pharma, Takeda, Topivert, UCB, VHsquared and Vifor, Conflict with: Oxford University Hospitals NHS Foundation Trust and the University of Oxford, Conflict with: Lecture fee(s): AbbVie, Amgen, Biogen, Ferring, Takeda
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 | 0,007 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| 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 ».