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S47 Treatment Persistence Among Anti-TNF-Experienced Patients With Ulcerative Colitis Switching to Ustekinumab or Cycling to Another Anti-TNF Agent

2023· article· en· W4389743734 on OpenAlexaff
Maryia Zhdanava, Sabree Burbage, Porpong Boonmak, Sumesh Kachroo, Patrick Lefèbvre, Aditi Shah, Bridget Godwin, Dominic Pilon

Bibliographic record

VenueThe American Journal of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsGroup for Research in Decision Analysis
Fundersnot available
KeywordsMedicineUstekinumabAdalimumabInfliximabGolimumabVedolizumabInternal medicineUlcerative colitisTumor necrosis factor alphaGastroenterologyDisease

Abstract

fetched live from OpenAlex

Background: Anti-TNF agents are often used as first-line biologic therapy in ulcerative colitis (UC). If the initial anti-TNF fails, switching to a different class of biologics or cycling to another anti-TNF agent may be necessary. Ustekinumab is a recently approved anti-interleukin 12/23 antibody for treatment of UC. This study compared real-world persistence in anti-TNF-experienced patients with UC who switched to ustekinumab or cycled to another anti-TNF agent. Methods: Adults with UC that discontinued treatment with an anti-TNF, and then switched to ustekinumab or cycled to another anti-TNF (index date) between 10/21/2019 and 03/02/2022 were selected from the IQVIA PharMetrics® Plus database. Patients had ≥12 months of continuous insurance eligibility before the first observed anti-TNF without UC-indicated biologics or advanced therapies. During the 12-months before the index date (baseline period), patients had no other immune disorders and discontinued the first anti-TNF. Baseline characteristics were balanced using inverse probability of treatment weights. Persistence was defined as no therapy gaps of >120 days (ustekinumab, infliximab) or >60 days (adalimumab, golimumab) between days of supply. Composite endpoints were persistence while on monotherapy (no immunomodulators/non-index biologics/advanced therapies) and persistence while being corticosteroid-free (< 14 consecutive days of corticosteroids supply after day 90 post-index). Endpoints were assessed at 12 months after the maintenance phase start using weighted Kaplan-Meier analyses and weighted Cox proportional hazards models adjusted for baseline gastroesophageal reflux disease, anal/rectum hemorrhage, and the baseline anti-TNF agent. Results: After weighting, there were 182 patients who switched to ustekinumab (mean age: 40.9; 47.4% female) and 129 patients who cycled to a different anti-TNF (mean age: 40.8; 47.7% female). In the cycle cohort, the index agent cycled to was infliximab for 53.3%, adalimumab for 40.1% and golimumab for 6.6%. At 12 months after maintenance phase start, 78.7% of the switch cohort and 63.8% of the cycle cohort persisted on the index biologic; persistence was 2.11 times higher in the switch cohort versus the cycle cohort (hazard ratio [HR]: 2.11; 95% confidence interval [CI]: 1.29-3.47; p-value: 0.0031). Moreover, 73.9% of the switch cohort and 49.1% of the cycle cohort persisted on the index biologic while on monotherapy; persistence while on monotherapy was 2.40 times higher in the switch cohort versus the cycle cohort (HR: 2.40; 95% CI: 1.57-3.65; p-value: < 0.001). Finally, 57.0% of the switch cohort and 48.3% of the cycle cohort were persistent and corticosteroid-free; the rate of being persistent and corticosteroid-free trended 31% higher in the switch cohort relative to the cycle cohort but was not statistically significant (HR: 1.31; 95% CI: 0.90-1.89; p-value 0.1607). Conclusions: After discontinuation of an anti-TNF, persistence on biologic therapy was better among patients with UC who switched to ustekinumab compared to those who cycled to a different anti-TNF. These findings may inform treatment strategies for patients on the first anti-TNF agent who require treatment adjustment.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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
Published2023
Admission routes1
Has abstractyes

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