A204 COST-EFFECTIVENESS OF THERAPIES AFTER FAILURE OF CONVENTIONAL THERAPY FOR PATIENTS WITH MODERATE-TO-SEVERE ULCERATIVE COLITIS IN THE CANADIAN HEALTHCARE SYSTEM
Bibliographic record
Abstract
Abstract Background Ulcerative colitis (UC) is a chronic inflammatory disease of the colon which requires ongoing medical therapy. The therapeutic options for moderate-to-severe UC include biologics and small molecules, which are effective but come with a significant cost. As such, their exact positioning in the therapeutic algorithm remains unclear. Purpose The aim of our study was to assess and compare the cost-effectiveness of infliximab, adalimumab, vedolizumab, golimumab, ustekinumab and tofacitinib for the management of moderate-to-severe UC from the perspective of the Canadian public healthcare system. Method A Markov model was constructed to simulate the disease course of UC patients after initiating each available therapy. Drug costs were obtained from the Alberta Health Drug Benefit List and the remaining costs were determined from the CIHI Patient Cost Estimator. Transition probabilities were obtained from a review of the literature, and loss of response and complication rates were obtained from randomized controlled trials. Our main analysis used a time horizon of 5 years, and time horizons of 1- and 10-years were also assessed in our sensitivity analysis. Probabilistic sensitivity analysis was performed to characterize uncertainty related to all parameters. Result(s) Infliximab costs $26,611 per quality-adjusted life year (QALY) using a 5-year time horizon. Adalimumab costs $20,783 per QALY. Vedolizumab costs $40,553 per QALY. Golimumab costs $34,316 per QALY. Ustekinumab costs $26,366 per QALY. Lastly, tofacitinib costs $25,572 per QALY. At a willingness-to-pay threshold of $50,000 per QALY, sensitivity analysis revealed that infliximab, adalimumab, vedolizumab, golimumab, ustekinumab and tofacitinib had a 36%, 12%, 1%, 1%, 44% and 6% probability of being cost-effective, respectively. Conclusion(s) Our economic model concluded that adalimumab is the most cost-effective first-line therapy for UC patients who have failed conventional therapy. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".