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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».