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S1469 Understanding Gastroenterologist 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· W4403722356 on OpenAlexaff
Stefan Schreiber, Alissa Walsh, Peter Hur, Laura Panattoni, Brett Hauber, G Gahlon, Josh Coulter, Karolina Wosik, Joseph C. Cappelleri, Nadya Prood, 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 colitisColitisIntensive care medicineGastroenterologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Introduction: As the number of advanced treatment options for moderately to severely active ulcerative colitis (UC) increases, it is necessary to understand the factors driving gastroenterologist (GE) choice when escalating patients from conventional to advanced therapy. Methods: We completed a quantitative analysis of GE therapy attribute preferences when choosing to escalate patients to their first advanced UC therapy. We conducted an online cross-sectional survey using a discrete choice experiment (DCE) design. Attribute and level selection was informed by a targeted literature search and formative qualitative research with patients and clinicians. Survey responders were practicing GEs experienced in treating patients with moderately to severely active UC, recruited from France, Germany, Italy, Spain, and the United Kingdom (UK). Preference weights were estimated using a random parameters logit model for varying levels of 7 attributes: time to symptom improvement, probability of remission at 1 year, difference between probability of remission and CS-free remission, 5-year risk of malignancy, annual risk of serious infection, annual risk of major adverse cardiovascular events, and mode and frequency of administration. Relative importance (RI) was calculated using the difference in preference weights between the most and least preferred level of each attribute, scaled from 0 to 100%. An additional survey section was included to understand GE treatment and prescribing practices. Results: A total of 397 GEs were included (France n = 140; Germany n = 40; Italy n = 40; Spain n = 47; UK n = 130). The most common GE-reported barriers to prescribing advanced therapies were concerns about contraindications and risks/side effects from patients (54.9%) and GEs (39.8%), perceived patient concerns about receiving injections or infusions (35.5%), and concerns about cost or insufficient reimbursement (32.2%). All DCE attributes factored into GE treatment decisions (see Table for RI and preference weights). The 3 most impactful attributes were probability of remission at 1 year (RI 48.4%), followed by 5-year risk of malignancy (RI 11.4%) and time to symptom improvement (RI 11.1%; Table 1). Conclusion: All attributes factored into the trade-offs GEs consider when escalating patients with moderately to severely active UC to their first advanced therapy. Whereas risk of side effects was the most stated GE barrier to prescribing advanced therapy, probability of remission outweighed all other DCE attributes. Table 1. - Preference weights and RI of attributes influencing advanced UC therapy choice (N = 397) Time to symptom improvement Probability of remission at 1 year Difference between probability of remission and CS-free remission Five-year risk of malignancy Annual risk of serious infection Annual risk of MACE Mode and frequency of administration Attribute RI a , % (95% CI) b 11.1(8.9, 13.4) 48.4(45.7, 51.1) 8.0(6.1, 10.0) 11.4(9.5, 13.2) 6.7(4.9, 8.5) 6.8(5.0, 8.6) 7.5(5.4, 10.1) Preference weight level (95% CI) c Level 1 2 weeks0.50(0.31, 0.69) 20% probability-2.11(-2.42, -1.81) 0% difference0.27(0.13, 0.41) 1 / 1000 patients0.49(0.35, 0.62) 1 / 100 patients0.27(0.14, 0.40) 1 / 1000 patients0.27(0.14, 0.40) Oral pill 1 – 2 times daily with potential dose change0.15(-0.02, 0.33) Level 2 4 weeks0.22(0.09, 0.34) 35% probability0.26(0.18, 0.34) 5% difference0.12(0.03, 0.21) 3 / 1000 patients-0.04(-0.13, 0.05) 3 / 100 patients0.02(-0.07, 0.10) 3 / 1000 patients0.01(-0.08, 0.10) Oral pill 1 – 2 times daily with the same dose throughout0.23(0.10, 0.35) Level 3 8 weeks-0.31(-0.44, -0.18) 45% probability1.85(1.65, 2.06) 15% difference-0.39(-0.48, -0.29) 5 / 1000 patients-0.45(-0.54, -0.35) 5 / 100 patients-0.28(-0.38, -0.19) 5 / 1000 patients-0.28(-0.38, -0.19) Injection every1 – 2 weeks0.01(-0.12, 0.14) Level 4 12 weeks-0.41(-0.55, -0.27) N/A N/A N/A N/A N/A Infusion every4 – 8 weeks-0.39(-0.52, -0.26) 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%.b95% CIs that do not include zero indicate a statistically significant RI of an attribute. All 7 attributes were statistically significantly important when 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 statistically significantly more important than all other attributes.cPreference 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 events; 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)0.036
metaresearch head score (Gemma)0.033
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 score0.036
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
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 head0.141
GPT teacher head0.384
Teacher spread0.243 · 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
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