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S1468 Understanding Patient Preferences at the Time of Treatment Escalation to First Line Advanced Therapies in Ulcerative Colitis: A Discrete Choice Experiment in Five European Countries

2024· article· en· W4403722096 on OpenAlexaff
Stefan Schreiber, Alissa Walsh, Peter Hur, Laura Panattoni, Brett Hauber, Grace Gahlon, Josh Coulter, Karolina Wosik, Joseph C. Cappelleri, Natalie Land, Xiang Guo, Anthony Buisson

Bibliographic record

VenueThe American Journal of Gastroenterology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsPfizer (Canada)
Fundersnot available
KeywordsMedicineUlcerative colitisSecond lineColitisIntensive care medicineFirst lineInternal medicineDisease

Abstract

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Introduction: Patients with moderately to severely active ulcerative colitis (UC) escalating from conventional therapy (CT) to advanced therapy (AT) often consider several treatment factors. As more AT options become available, understanding the treatment preferences of AT naïve patients will enhance shared decision making with clinicians. Methods: We conducted an online, cross-sectional survey of patients in the EU and UK with self-reported moderately to severely active UC who were diagnosed ≥ 3 months ago, had history of CT use (5-ASA, steroids, or immunomodulators), and did not report history of AT use. A discrete choice experiment (DCE) was used to quantify patient preferences for attributes associated with first-line AT for UC, including efficacy, safety, and mode and frequency of administration. Each patient completed 12 DCE choice tasks. Preference weights were estimated for all attribute levels using a random parameters logit model. Attribute relative importance (RI; range 0−100%) was calculated using the difference in preference weights between the most and least preferred level of each attribute to summarize the relative influence of each attribute on treatment choice. Results: 514 patients, with a mean age of 44.0 years, were included. The majority had a university degree (73.0%), were employed full-time (53.7%), diagnosed with UC ≤ 5 years ago (76.1%), and had a mean (SD) Patient Modified Simple Clinical Colitis Activity Index score of 4.9 (3.3; score range 0−19; a score of < 3 is generally defined as remission). All DCE attributes factored into patient treatment decisions (Table: RI and preference weights). However, probability of remission at 1 year had the strongest influence on patients’ treatment preferences (RI: 46.2%) followed by 5-year risk of cancer (RI: 11.0%), annual risk of a major adverse cardiovascular event (RI: 10.9%) and time to symptom improvement (RI: 8.5%). Oral formulations with the same dose throughout were preferred over injection or infusion options (Table 1). Conclusion: In patients with moderate to severe UC and no reported history of AT use, probability of remission at 1 year, followed by 5-year risk of cancer, were the most important attributes influencing treatment choice, though all attributes tested had some impact. These findings will help to highlight to clinicians the trade-offs between efficacy, safety, and administration mode that patients make when considering AT choices. This is becoming increasingly important as more treatment options become available. Table 1. - Preference weights and relative importance of attributes influencing advanced uc therapy choice (n=514) Probability of remission at 1 year 5-year risk of cancer Annual risk of MACE Time to symptom improvement Mode and frequency of administration Probability of CS-free remission at 1 year Annual risk of serious infection Attribute RI, % (95% CI) a 46.2(43.1, 49.3) 11.0(9.0, 12.9) 10.9(8.9, 12.9) 8.5(6.2, 11.1) 8.2(5.5, 10.8) 7.7(5.7, 9.7) 7.6(5.5, 9.6) Preference weight level (95% CI) b Level 1 20% probability-1.49(-1.71, -1.27) 1 / 1,000 patients0.35(0.24, 0.46) 1 / 1,000 patients0.31(0.21, 0.42) 2 weeks0.23(0.09, 0.38) Oral pill 1 – 2 times daily with the same dose throughout0.25(0.15, 0.36) 0% difference0.22(0.11, 0.33) 1 / 100 patients0.24(0.13, 0.34) Level 2 35% probability0.15(0.08, 0.23) 3 / 1,000 patients-0.03(-0.10, 0.05) 3 / 1,000 patients0.04(-0.03, 0.11) 4 weeks0.19(0.08, 0.29) Oral pill 1 – 2 times daily with potential dose change0.10(-0.04, 0.25) 5% difference0.03(-0.04, 0.11) 3 / 100 patients-0.01(-0.08, 0.06) Level 3 45% probability1.33(1.19, 1.48) 5 / 1,000 patients-0.32(-0.40, -0.24) 5 / 1,000 patients-0.35(-0.44, -0.27) 8 weeks-0.13(-0.23, -0.03) Injection every1 – 2 weeks-0.11(-0.21, -0.00) 15% difference-0.25(-0.33, -0.17) 5 / 100 patients-0.23(-0.30, -0.15) Level 4 N/A N/A N/A 12 weeks-0.29(-0.40, -0.18) Infusion every4 – 8 weeks-0.25(-0.36, -0.14) N/A N/A The DCE model included 7 attributes, each with several preference weight levels.aRI is calculated as the difference in preference weights between the most preferred and least preferred level divided by the sum of the differences across all attributes; estimates sum to 100%. 95% CIs that do not include zero indicate a statistically significant RI of an attribute. All 7 attributes factored into the decision of selecting an advanced therapy. 95% CIs that do not overlap for pairs of attributes indicate a statistically significant difference in importance between attributes. Probability of remission at 1 year was significantly more important than all other attributes.bPreference weight levels are effects coded; zero indicates the mean effect across all attribute levels.CI, confidence interval; CS, corticosteroid; DCE, discrete choice experiment; MACE, major adverse cardiovascular event; n, total number of patients; N/A, not applicable; RI, relative importance; UC, ulcerative colitis.

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)0.029
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.122
GPT teacher head0.371
Teacher spread0.250 · 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 designSimulation or modeling
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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