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Record W4406702688 · doi:10.1093/ecco-jcc/jjae190.0906

P0732 A cost comparison of treatment sequencing for Ulcerative Colitis: vedolizumab vs ustekinumab as a second-line biologic in anti-TNF-exposed patients in Canada

2025· article· en· W4406702688 on OpenAlexaffabout
Montserrat Martín‐Baranera, Jing Zhou, Stephen Mac, K. Kao, Karissa Johnston, John K. Marshall

Bibliographic record

VenueJournal of Crohn s and Colitis · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcMaster UniversityTakeda (Canada)
Fundersnot available
KeywordsVedolizumabUstekinumabMedicineUlcerative colitisInflammatory bowel diseaseInternal medicineTumor necrosis factor alphaAdalimumabCrohn's diseaseGastroenterologyDermatologyImmunologyDisease

Abstract

fetched live from OpenAlex

Abstract Background Ulcerative colitis (UC) is a chronic inflammatory condition characterized by periods of relapse and remission. The main therapeutic goal is to induce and maintain remission to prevent long-term disease progression.1 Given the nature of the condition, patients often cycle through multiple treatments.2 The objective was to evaluate overall costs of treatment sequencing for anti-tumor necrosis factor (TNF)-exposed UC patients from a societal perspective in Canada. Methods A cost-comparison model was developed to investigate two treatment sequences in a hypothetical cohort of anti-TNF-exposed adult (≥ 18 years) UC patients: vedolizumab as a second-line biologic followed by ustekinumab vs. ustekinumab as a second-line biologic followed by vedolizumab. The model time horizon was based on the longest treatment duration from first-line anti-TNF to the end of third-line therapy, which was 8.23 years according to published sources. Treatment persistence was characterized by parametric time-to-discontinuation curves per line of therapy from published literature. Where multiple sources were identified, curves were fitted to pooled data. Treatment costs were based on dosing regimens described in Health Canada product monographs and parametric curves fitted to treatment persistence data. For fourth-line therapy and beyond, patients received a validated mix of subsequent treatment options including upadacitinib, mirikizumab, etrasimod, and colectomy. Costs of treatment, healthcare resource use, adverse event management, and productivity loss were aggregated, with total costs reported. Several scenario analyses assessed base case robustness; statistical testing was not conducted. Results All patients were treated with a first-line anti-TNF regimen for 1.97 years. Patients who then received second-line vedolizumab remained on second-line treatment longer versus those who received second-line ustekinumab (3.79 vs 2.52 years). Patients who received second-line vedolizumab subsequently received ustekinumab for 2.48 years (end of time horizon); for those receiving second-line ustekinumab, patients were subsequently treated with vedolizumab for 3.16 years and the composite mix for 0.59 years. Both treatment sequences yielded similar per-patient-per-year total costs: $203,107 for second-line vedolizumab and $201,988 for second-line ustekinumab. Conclusion Across lines of therapy, treatment persistence for vedolizumab has been reported as superior to ustekinumab. Given the comparable overall costs, the value to patients of extending the duration of earlier lines of therapy and slowing transition to later lines should be considered when treating moderate-to-severe UC. References 1.Wetwittayakhlang P, Lontai L, Gonczi L, et al. Treatment Targets in Ulcerative Colitis: Is It Time for All In, including Histology? J Clin Med. 2021;10(23). 2.Bressler B. Is there an optimal sequence of biologic therapies for inflammatory bowel disease? Therap Adv Gastroenterol. 2023;16:17562848231159452.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.081
Threshold uncertainty score0.591

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.015
GPT teacher head0.272
Teacher spread0.258 · 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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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