MétaCan
Menu
← Retour à la cohorte
Enregistrement W4316086120 · doi:10.14309/01.ajg.0000859848.43617.3c

S802 Persistence Among Patients With Crohn’s Disease Previously Treated With an Anti-tumor Necrosis Factor Inhibitor and Switching or Cycling to Another Biologic Agent

2022· article· en· W4316086120 sur OpenAlexaff
Maryia Zhdanava, Sumesh Kachroo, Ameur M. Manceur, Zhijie Ding, Christopher Holiday, Ruizhi Zhao, Bridget Goodwin, Dominic Pilon

Notice bibliographique

RevueThe American Journal of Gastroenterology · 2022
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueInflammatory Bowel Disease
Établissements canadiensGroup for Research in Decision Analysis
Organismes subventionnairesnon disponible
Mots-clésAdalimumabMedicineVedolizumabInfliximabUstekinumabInternal medicinePersistence (discontinuity)DiscontinuationTumor necrosis factor alphaCrohn's diseaseGastroenterologyDisease

Résumé

récupéré en direct d'OpenAlex

Introduction: Among patients with Crohn’s disease (CD), non-response to an anti-TNF agent can lead to switching to a biologic in a different class (i.e., ustekinumab, vedolizumab) or cycling to another anti-TNF agent (i.e., adalimumab, infliximab, certolizumab). This study compared real-world persistence among patients with CD who switch or cycle from an anti-TNF agent. Methods: Adults with CD treated with an anti-TNF whose first switching or cycling (index date) occurred between 09/23/2016 and 08/01/2019 were selected from the IBM® MarketScan® Commercial Database. Patients had: ≥12 months of continuous insurance eligibility before the first anti-TNF, discontinuation of the first anti-TNF within 12 months (baseline period) of the index date, and no other immune disorders in the 12-month baseline period. Cohorts were balanced on baseline characteristics using inverse probability of treatment weights (IPTW). Persistence to index biologic (i.e., biologic switched or cycled to) was defined as absence of therapy exposure gaps >120 days (ustekinumab, vedolizumab, infliximab) or >60 days (adalimumab, certolizumab) between days of supply. Composite endpoints were: persistence and being corticosteroid-free (no corticosteroids with ≥14 days of supply after day 90 post-index), and persistence while on monotherapy (no immunomodulators/non-index biologics). Weighted Kaplan-Meier and Cox models were used to assess outcomes at 12 months post-index. Results: After IPTW, the sample size was 444 and 441 in the switching and cycling cohorts, and baseline characteristics were well balanced (Table). At 12 months post-index, the proportions of patients persistent to the index agent and patients persistent while on-monotherapy were significantly higher in the switching compared to the cycling cohort (Figure). In the switching compared to the cycling cohort, the rate of being persistent to the index agent was 44% higher (hazard ratio [HR]: 1.44; 95% confidence interval [CI]: 1.11-1.88; P=0.007*), the rate of being persistent and corticosteroid-free 8% higher (HR: 1.08; 95% CI: 0.89-1.32; P=0.426), and the rate of being persistent while on-monotherapy 56% higher (HR: 1.56; 95% CI: 1.28-1.90; P< 0.001*). Conclusion: Following the discontinuation of the first anti-TNF agent, patients with CD who switched to a different class of biologic were more persistent than patients who cycled to another anti-TNF agent. These findings may aid physicians whose patients experience loss of response on the first anti-TNF agent.Figure 1.: CD: Crohn’s disease; Std diff: standardized difference; SD: standard deviation; TNF: tumor necrosis factor; 5-ASA: 5-aminosalicylic acid Table: Selected baseline characteristics in weighted switching and cycling cohorts1,2. Notes: (1) cohorts were weighted on baseline characteristics using inverse probability of treatment weights; characteristics considered well balanced if standardized difference is <10%; (2) patients receiving immunomodulators or corticosteroids (at least one episode of ≥90 days of continuous use), patients with CD-related hospitalizations or CD-related surgeries. Table 1. - CD: Crohn’s disease Mean ± SD or n (%) SwitchingN=444 CyclingN=441 Std diff (%) Age 40.4 ± 14.2 39.5 ± 13.9 6.3 Female 250 (56.3%) 257 (58.4%) 4.3 All-cause costs (US$ 2021) 72,594 ± 52,331 71,643 ± 53,192 1.8 Prescription drug costs 35,134 ± 29,132 34,340 ± 30,674 2.7 Total medical costs 37,459 ± 51,598 37,303 ± 52,046 0.3 Claims-derived CD severity indicator2 263 (59.1%) 266 (60.5%) 2.7 Charlson Comorbidity Index 0.54 ± 0.9 0.52 ± 0.9 1.4 CD-related surgery 38 (8.6%) 37 (8.4%) 0.9 Medication Corticosteroids 343 (77.1%) 331 (75.0%) 4.8 ≥1 episode with ≥60 days of continuous corticosteroid use 137 (30.9%) 144 (32.7%) 3.9 5-ASA 150 (33.7%) 138 (31.3%) 5.1 Immunomodulators 149 (33.5%) 154 (34.9%) 2.9 Antidiarrheals 26 (5.8%) 27 (6.0%) 0.9 Baseline anti-TNF Adalimumab 288 (64.9%) 278 (63.1%) 3.7 Infliximab 140 (31.4%) 148 (33.5%) 4.4 Certolizumab pegol 16 (3.7%) 15 (3.4%) 1.6 Std diff: standardized difference; SD: standard deviation; TNF: tumor necrosis factor; 5-ASA: 5-aminosalicylic acid Table: Selected baseline characteristics in weighted switching and cycling cohorts1,2. Notes: (1) 1. Cohorts were weighted on baseline characteristics using inverse probability of treatment weights; characteristics considered well balanced if standardized difference is <10%; (2) Patients receiving immunomodulators or corticosteroids (at least one episode of ≥90 days of continuous use), patients with CD-related hospitalizations or CD-related surgeries

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)0,001
score de la tête « metaresearch » (Gemma)0,002
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 enseignants0,004
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,008
Tête enseignante GPT0,214
Écart entre enseignants0,206 · 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
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueThe American Journal of Gastroenterology→Même sujetInflammatory Bowel Disease→Travaux en français237 207→