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Enregistrement W4403722096 · doi:10.14309/01.ajg.0001035240.85540.35

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 sur 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

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 colitisSecond lineColitisIntensive care medicineFirst lineInternal medicineDisease

Résumé

récupéré en direct d'OpenAlex

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.

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)0,029
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,154

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0290,026
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,002
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,122
Tête enseignante GPT0,371
Écart entre enseignants0,250 · 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'étudeSimulation ou modélisation
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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