Impact of Disease Factors of Patients with Psoriasis and Psoriatic Arthritis on Biologic Therapy Switching: Real-World Evidence from the CorEvitas Psoriasis Registry
Notice bibliographique
Résumé
Patients with psoriasis (PSO) and psoriatic arthritis (PsA) may frequently switch biologic therapies over the course of treatment because of symptom variability and individual responses. Real-world studies analyzing patient characteristics and clinical factors associated with biologic switching are limited. This longitudinal cohort study used real-world data from the CorEvitas Psoriasis Registry to evaluate the relationship between associated disease factors and biologic switching among patients with PSO and PsA in the United States (US) and Canada following initiation of a biologic. Patients were evaluated between April 2015–August 2022. Combinations of disease severity (as measured by Psoriasis Area Severity Index [PASI]) and Dermatology Life Quality Index (DLQI) as a measure of health-related quality of life (HRQoL) were assessed, and the association with time to switching was calculated using Cox proportional hazards regression modeling. Among 2580 patient-initiations (instances of patients initiating a biologic), 504 (19.5%) switched biologics within 30 months of initiation. Switching was more frequent when either PASI > 10 or DLQI > 5 compared with PASI ≤ 10 or DLQI ≤ 5 at follow-up. Patients with higher skin involvement (PASI > 10) and impact on HRQoL (DLQI > 5) were 14 times more likely to switch (hazard ratio = 14.2, 95% confidence interval: 10.7, 18.9) than those with lower skin involvement (PASI ≤ 10) and HRQoL (DLQI ≤ 5). Patients with PSO and PsA treated in a real-world dermatology setting with substantial disease factors following biologic initiation were more likely to switch therapies. Those with PASI > 10 and DLQI > 5 switched more frequently than those with PASI ≤ 10 and DLQI ≤ 5. Many patients with psoriasis may also have a related condition called psoriatic arthritis. Biologic medications work by helping to reduce inflammation and are commonly used to treat the symptoms of psoriasis and psoriatic arthritis. Patients might not all respond the same way to treatment and may need to change their medications over time. It is important we understand the reasons for switching medications to help patients better manage their symptoms. This study used information from a database on patients with both psoriasis and psoriatic arthritis. The database includes information on patients’ medical history, including when they start and change their medication. We looked at data from patients who switched medications and patients who did not switch medications and examined differences in both how serious a doctor found their disease and the patients’ own opinions of their overall health. We found that patients were more likely to change their biologic medication if they had more difficult psoriasis and psoriatic arthritis symptoms that caused worse skin problems, joint pain, and effects on their overall health compared with patients who had not changed their medication. These results suggest that it is important to consider both how serious a doctor finds their disease and patients’ opinions of how much their symptoms affect their overall health. Understanding the reasons why patients switch medications will help to develop better ways of managing psoriasis and psoriatic arthritis.
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 | 0,000 | 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 | 0,000 |
| 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 ».