S966 Persistence Among Patients with Ulcerative Colitis Previously Treated with an Anti-Tumor Necrosis Factor Inhibitor and Switching or Cycling to Another Biologic Agent
Notice bibliographique
Résumé
Introduction: In ulcerative colitis (UC), anti-TNF agents often are first-line biologic therapy. Switching to a different class of biologics (ustekinumab, vedolizumab) or cycling to another anti-TNF agent (adalimumab, infliximab, golimumab) is necessary if an initial anti-TNF fails. Real-world persistence in patients with UC who switch or cycle from an anti-TNF agent was compared. Methods: Adults with UC treated with an anti-TNF, who switched or cycled (index date) between 10/21/2019 and 03/02/2022, were selected from the IQVIA PharMetrics® Plus database. Patients had ≥12 months of continuous insurance eligibility before the first anti-TNF without UC-indicated biologics or advanced therapies. During the 12-months before the index date (baseline period), patients had no other immune disorders and discontinued the first anti-TNF. Baseline characteristics were balanced using inverse probability of treatment weights. Persistence on index biologic was defined as no therapy exposure gaps >120 days (ustekinumab, vedolizumab, infliximab) or >60 days (adalimumab, golimumab) between days of supply. Composite endpoints were persistence and being corticosteroid-free (< 14 consecutive days of corticosteroids supply after day 90 post-index), and persistence while on monotherapy (no immunomodulators/non-index biologics/advanced therapies). Endpoints were assessed with weighted Kaplan-Meier and Cox proportional hazards models at 12 months after maintenance phase start. Results: The switch and cycle cohorts included 488 and 129 patients, respectively; baseline characteristics were well balanced (Table 1). At 12 months after maintenance phase start, proportions of persistent patients and patients persistent on monotherapy were significantly higher in the switch vs the cycle cohort (Figure 1). In the switch cohort, the rate of persistence was 53% higher (hazard ratio [HR]: 1.53; 95% confidence interval [CI]: 1.10-2.12), the rate of persistence and being corticosteroid-free 23% higher (HR: 1.23; 95% CI: 0.93-1.63), and the rate of persistence while on-monotherapy was 2 times higher (HR: 2.18; 95% CI: 1.64-2.92) than in the cycle cohort. Conclusion: Patients with UC who switched from an anti-TNF agent to a different class of biologic were more persistent than patients who cycled to another anti-TNF agent. Findings may aid physicians whose patients experience treatment failure on the first anti-TNF agent. Funded by Janssen Scientific Affairs, LLC.Figure 1.: Kaplan-Meier curves in weighted* switch and cycle cohorts of being: a) persistent to index biologic, b) persistent and corticosteroid-free, c) persistent while on monotherapy. *Cohorts were weighted on baseline characteristics using inverse probability of treatment weights. Table 1. - Selected baseline characteristics in weighted switch and cycle cohorts** Mean ± SD [median] or n (%) SwitchN=488 CycleN=129 Std diff (%) Age (years) 41.4 ± 13.9 [40.8] 40.7 ± 12.6 [40.2] 5.4 Female 219 (44.9) 57 (43.8) 2.1 Any intestinal complication 71 (14.5) 22 (17.3) 7.8 Selected general comorbid condition Diarrhea 269 (55.2) 73 (56.8) 3.2 Pain 219 (44.8) 60 (46.2) 2.8 Anemia 164 (33.6) 40 (31.2) 5.2 UC-related medication Baseline anti-TNF Adalimumab 295 (60.5) 73 (56.9) 7.5 Infliximab and biosimilars 188 (38.6) 56 (43.1) 9.2 Golimumab 4 (0.8) 0 (0.0) 12.9* Corticosteroids 387 (79.4) 98 (75.8) 8.7 5-ASA 324 (66.3) 80 (62.2) 8.5 Immunomodulators 84 (17.2) 21 (16.4) 2.1 Antidiarrheals 28 (5.8) 6 (4.4) 6.4 Concomitant medication Opioids 166 (34.1) 43 (33.2) 1.9 Antibiotics 139 (28.6) 38 (29.2) 1.5 All-cause costs (US$ 2022) 62,103 ± 46,224 [54,686] 60,596 ± 35,758 [57,779] 3.7 Prescription drug costs 37,478 ± 35,277 [35,119] 37,298 ± 34,639 [29,749] 0.5 Medical costs 24,626 ± 40,172 [13,650] 23,298 ± 27,065 [13,113] 3.9 **Cohorts were weighted on baseline characteristics using inverse probability of treatment weights; characteristics considered well balanced if standardized difference is <10%. SD: standard deviation; Std diff: standardized difference; TNF: tumor necrosis factor; UC: ulcerative colitis; 5-ASA: 5-aminosalicylic acid *denotes standardized difference ≥10%.
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 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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».