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Enregistrement W4403722356 · doi:10.14309/01.ajg.0001035244.07582.cf

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 sur 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

Notice bibliographique

RevueThe American Journal of Gastroenterology · 2024
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealth Systems, Economic Evaluations, Quality of Life
Établissements canadiensPfizer (Canada)
Organismes subventionnairesnon disponible
Mots-clésMedicineUlcerative colitisColitisIntensive care medicineGastroenterologyInternal medicineDisease

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,036
score de la tête « metaresearch » (Gemma)0,033
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,190

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0360,033
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0030,001
Science ouverte0,0010,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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.

Tête enseignante Opus0,141
Tête enseignante GPT0,384
Écart entre enseignants0,243 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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