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Record W4400193042 · doi:10.1136/gutjnl-2024-bsg.31

O31 Tofacitinib versus vedolizumab among bio-naïve patients with ulcerative colitis: a real-world propensity-weighted comparison

2024· article· en· W4400193042 on OpenAlexaboutno aff
Beatriz Gros, Nathan Constantine‐Cooke, J D Kennedy, Alexander T. Elford, Claire O’Hare, Colin Noble, Gareth‐Rhys Jones, Ian Arnott, Nikolas Plevris, Charlie W. Lees

Bibliographic record

VenueOral Presentations · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsTofacitinibVedolizumabUlcerative colitisMedicineInternal medicineGastroenterologyDisease

Abstract

fetched live from OpenAlex

Introduction Over the last decade, treatment options for moderate-to-severe ulcerative colitis (UC) have expanded. However, comparative studies between these agents are limited, especially among biologic naïve patients. There are sparse data comparing biologics with small molecules for IBD. We aimed to compare the efficacy, safety and persistence of tofacitinib and vedolizumab as the first advanced treatment for patients with UC. Methods Patients who received tofacitinib or vedolizumab as first advanced therapy for UC in NHS Lothian were included. We capped the upper age limit at 65 years to take into account regulatory guidelines for the use of JAKi as first line therapies across IMIDs. To allow treatment effect to be assessed outside of a randomized trial, we used inverse probability of treatment weighting (IPTW). This approach takes the probability of treatment assignment into account without potentially drastically reducing the analysable cohort size. The probability of treatment assignment was calculated via logistic regression using age, gender, IBD duration, Montreal extent, CRP, concomitant corticosteroids and partial Mayo score at drug commencement. Missing data for these variables were imputed using multivariate imputation by chained equations. Confounder-adjusted survival curves were created using Kaplan-Meier estimates weighted via IPTW. The Pepe and Fleming test was used to compare survival curves from drug commencement to day 1000 of treatment. Results We included 158 patients of whom 81 (51.2%) received vedolizumab and 77 (48.7%) tofacitinib. Median follow-up for patients on vedolizumab was 3.1 (1.6–4.8) years and for tofacitinib 1.5 (0.34–2.3) years. Baseline demographics were comparable except for disease extent (Montreal E3 58% vedolizumab versus 31.2% tofacitinib, p=0.001). At drug commencement there were no differences in steroid prescription (60.5% vedolizumab versus 57.1% tofacitinib), partial Mayo score, CRP or faecal calprotectin. At week 12, steroid free clinical remission was more frequent in the vedolizumab group (69% vs 51.4%, p=0.030) figure 1B. Vedolizumab persistence was superior to tofacitinib (p=0.005) figure 1A. Primary non-response and secondary loss of response were 9.9% and 17.3% for vedolizumab and 23.4% and 13% for tofacitinib respectively. There were no differences in the frequency of adverse events (11 [13.6%] vedolizumab vs 19 [24.7%] tofacitinib, p=0.629). However, adverse events resulting in temporary discontinuation of the drug occurred in 1 (1.2%) vedolizumab versus 11 (14.3%) tofacitinib, p=0.013. Conclusions We found that the persistence and tolerability of vedolizumab was superior to tofacitinib in bionaive UC, although the rates of clinical and biomarker remission were comparable. These data may help inform positioning of advanced therapy in 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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.302
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes1
Has abstractyes

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