Comparative effectiveness of biologic agents for the treatment of psoriasis in a real-world setting: Results from a large, prospective, observational study (Psoriasis Longitudinal Assessment and Registry [PSOLAR])
Notice bibliographique
Résumé
BackgroundComparing effectiveness of biologics in real-world settings will help inform treatment decisions.ObjectivesWe sought to compare therapeutic responses among patients initiating infliximab, adalimumab, or etanercept versus ustekinumab during the Psoriasis Longitudinal Assessment and Registry (PSOLAR).MethodsProportions of patients achieving a Physician Global Assessment score of clear (0)/minimal (1) and mean decrease in percentage of body surface area with psoriasis were evaluated at 6 and 12 months. Adjusted logistic regression (Physician Global Assessment score 0/1) and analysis of covariance (percentage of body surface area with psoriasis) were performed to determine treatment factors associated with effectiveness.ResultsOf 2541 new users on registry, 2076 had efficacy data: ustekinumab (n = 1041), infliximab (n = 116), adalimumab (n = 662), and etanercept (n = 257). Patients receiving tumor necrosis factor-alpha(-α) inhibitors were significantly less likely to achieve Physician Global Assessment score 0/1 versus ustekinumab (infliximab [odds ratio {OR} 0.396, P < .0001], adalimumab [OR 0.686, P = .0012], etanercept [OR 0.554, P = .0003] at 6 months and infliximab [OR 0.449, P = .0040] at 12 months). Mean decrease in percentage of body surface area with psoriasis was significantly greater for ustekinumab versus adalimumab (point estimate 1.833, P = .0020) and etanercept (point estimate 3.419, P < .0001) at 6 months and versus infliximab (point estimate 3.945, P = .0005) and etanercept (point estimate 2.778, P = .0007) at 12 months.LimitationsTreatment selection bias and limited data for doing adjustments are limitations.ConclusionsIn PSOLAR, effectiveness of ustekinumab was significantly better versus all 3 tumor necrosis factor-α inhibitors studied for the majority of comparisons at 6 and 12 months. Comparing effectiveness of biologics in real-world settings will help inform treatment decisions. We sought to compare therapeutic responses among patients initiating infliximab, adalimumab, or etanercept versus ustekinumab during the Psoriasis Longitudinal Assessment and Registry (PSOLAR). Proportions of patients achieving a Physician Global Assessment score of clear (0)/minimal (1) and mean decrease in percentage of body surface area with psoriasis were evaluated at 6 and 12 months. Adjusted logistic regression (Physician Global Assessment score 0/1) and analysis of covariance (percentage of body surface area with psoriasis) were performed to determine treatment factors associated with effectiveness. Of 2541 new users on registry, 2076 had efficacy data: ustekinumab (n = 1041), infliximab (n = 116), adalimumab (n = 662), and etanercept (n = 257). Patients receiving tumor necrosis factor-alpha(-α) inhibitors were significantly less likely to achieve Physician Global Assessment score 0/1 versus ustekinumab (infliximab [odds ratio {OR} 0.396, P < .0001], adalimumab [OR 0.686, P = .0012], etanercept [OR 0.554, P = .0003] at 6 months and infliximab [OR 0.449, P = .0040] at 12 months). Mean decrease in percentage of body surface area with psoriasis was significantly greater for ustekinumab versus adalimumab (point estimate 1.833, P = .0020) and etanercept (point estimate 3.419, P < .0001) at 6 months and versus infliximab (point estimate 3.945, P = .0005) and etanercept (point estimate 2.778, P = .0007) at 12 months. Treatment selection bias and limited data for doing adjustments are limitations. In PSOLAR, effectiveness of ustekinumab was significantly better versus all 3 tumor necrosis factor-α inhibitors studied for the majority of comparisons at 6 and 12 months.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».