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
Bibliographic record
Abstract
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.
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".