PTH-067 Incidence of pneumonia and other respiratory tract infections with vedolizumab treatment: clinical trial experience
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".