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P039 Integrating Maintenance Efficacy and Safety of Vedolizumab and Other Advanced Therapies for the Treatment of Ulcerative Colitis: A Network Meta-Analysis

2019· article· en· W2992389128 on OpenAlexaff
Vipul Jairath, Keith Chan, Karen Lasch, Sam Keeping, Christian Agboton, Aimee Blake, Jeroen Jansen, Haridarshan Patel

Bibliographic record

VenueThe American Journal of Gastroenterology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsPrecision Nanosystems (Canada)London Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineVedolizumabTofacitinibInternal medicineGolimumabUlcerative colitisInfliximabAdalimumabRandomized controlled trialDiscontinuationAdverse effectUstekinumabMaintenance therapyPopulationSurgeryRheumatoid arthritisTumor necrosis factor alphaChemotherapy

Abstract

fetched live from OpenAlex

BACKGROUND: Comparative efficacy and safety data for therapeutic options in ulcerative colitis (UC) are lacking. Intravenous (IV) vedolizumab (VDZ) was recently shown to be superior to subcutaneous (SC) adalimumab (ADA) in VARSITY, the first head-to-head randomized controlled trial (RCT) of biologic therapies in UC. The objective of this study is to provide estimates of the efficacy and safety of other biologic therapies and tofacitinib (TOFA) relative to VDZ IV, by means of a network meta-analysis (NMA). METHODS: Relevant RCTs of VDZ (IV and SC), ADA, infliximab (IFX), golimumab (GOL), ustekinumab (UST), and tofacitinib (TOFA) were identified through a targeted literature review. Efficacy outcomes in the maintenance period were remission and response at 52/54 weeks. Differences in study design (treat-through vs re-randomized) across the relevant RCTs were accounted for by assessing efficacy outcomes conditional on response at start of maintenance. For treat-through studies, this was response at 6/8 weeks. Safety outcomes were overall adverse events (AEs), serious AEs (SAEs), overall infections, serious infections, and AEs leading to discontinuation as reported at 52/54 weeks. Odds ratios (ORs) with 95% credible intervals (CrIs) were estimated using probit and binomial NMA models, with results presented with VDZ IV 300 mg Q8W as the reference group. Analyses were conducted for the overall study population, as well as separately for the anti-tumor necrosis factor (TNF)–naïve and –experienced populations. RESULTS: Sixteen RCTs evaluating 13 therapies were included in the NMAs. Connected networks could be created for all three populations in the maintenance (10 trials) period. In the overall population, relative to VDZ 300 mg Q8W, ADA 40 mg, GOL 50 mg, and UST 90 mg Q12W had significantly lower rates for maintenance of response (OR: 0.62 [95% CrI 0.45, 0.86], 0.54 [95% CrI 0.31, 0.94], 0.58 [95% CrI 0.34, 0.98], respectively) and maintenance of remission (OR: 0.62 [95% CrI 0.45, 0.86], 0.54 [95% CrI 0.30, 0.94], 0.58 [95% CrI 0.34, 0.98], respectively). All other treatments had similar maintenance of efficacy to VDZ 300 mg Q8W. For safety outcomes, GOL had significantly higher rates of overall AEs (GOL 100 mg OR: 2.17 [95% CrI 1.18, 4.00]; GOL 50 mg OR: 1.90 [95% CrI 1.02, 3.54]) relative to VDZ 300 mg Q8W. Both GOL 100 mg (OR: 1.84 [95% CrI 1.07, 3.18]) and TOFA 10 mg (OR: 2.03 [95% CrI 1.19, 3.55]) had significantly higher rates of infections, while UST 90 mg Q12W had significantly lower rates of infections (OR: 0.58 [95% CrI 0.33, 0.98]) relative to VDZ 300 mg Q8W. GOL 100 mg also had significantly higher rates of discontinuation due to AEs (OR: 3.43 [95% CrI 1.18, 10.22]). ADA 40 mg, IFX 10 mg/kg, and IFX 5 mg/kg were similar to VDZ 300 mg Q8W across all safety outcomes. CONCLUSION(S): Results from this NMA based on RCTs indicate a favorable benefit-risk profile for VDZ 300 mg Q8W compared with other advanced therapeutic options available for UC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.039
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0110.057
Bibliometrics0.0050.007
Science and technology studies0.0000.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.013
GPT teacher head0.266
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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