P790 Assessment of steroid use in patients with active ulcerative colitis who initiated a new Janus kinase inhibitor or tumour necrosis factor inhibitor using data from a United States claims database
Notice bibliographique
Résumé
Abstract Background In 2021, the United States (US) FDA issued a label update limiting the use of Janus kinase inhibitors (JAKi) to after tumour necrosis factor inhibitors (TNFi). We examined the rate of steroid use and treatment failure among patients (pts) with ulcerative colitis (UC) initiating a JAKi vs TNFi using data from a US claims database. Methods A database of adjudicated medical and pharmacy claims (IQVIA PharMetrics Plus; 2007–2022) was utilised to select pts with UC starting either a new JAKi/TNFi on/after 30 May 2018. Pts were followed from index date to the end of the study period, event of interest or treatment switch (whichever came first). The study assessed steroid use within 90 days of index date, and also treatment failure over the first 6 months after index date, defined as a composite of any: hospitalisation related to UC/colectomy (inpatient/emergency room)/switch to another advanced treatment (AT)/steroid use ≥90 days after index treatment initiation. Individual components of treatment failure were also analysed. Stabilised inverse probability treatment weights (sIPTW) were calculated using 19 confounders. Cox proportional hazards models with sIPTW were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Additional analyses stratifying by AT-naïve vs AT-experienced pts were conducted. Results In total, 6019 pts were included; 763 initiated a JAKi and 5256 a TNFi. More pts initiating a TNFi had prior use of non-biologic conventional treatments and were on concomitant conventional treatments at index date, compared with pts initiating a JAKi. Prior AT was more prevalent among JAKi- (74.3%) vs TNFi- (15.3%) treated pts; baseline steroid use was generally similar across groups (Table). Overall, there was a significantly lower risk of steroid use within 90 days in pts initiating a JAKi vs a TNFi (HR 0.75 [95% CI 0.65, 0.87]), and among AT-naïve and AT-experienced pts (HR 0.79 [95% CI 0.64, 0.97] and HR 0.69 [95% CI 0.59, 0.82], respectively; Figure a). There was a significantly lower risk of treatment failure with JAKi vs TNFi (HR 0.80 [95% CI 0.68, 0.94]; Figure b). A significantly lower risk of steroid use ≥90 days was found amongst JAKi- vs TNFi-treated pts overall and within AT-naïve and AT-experienced subgroups (Figure b–d). Conclusion In this large US-based claims analysis, pts with UC treated with a JAKi had significantly less steroid use than those treated with a TNFi; this was consistent in AT-naïve and AT-experienced pts. Limitations included confounding of factors uncontrollable in claims data and inherent bias due to approved use of JAKi in the US only in pts with inadequate response to TNFi. Study sponsored by Pfizer. Medical writing support provided by C Duncan, CMC Connect; funded by Pfizer.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».