Treatment Switch Patterns and Healthcare Costs in Biologic-Naive Patients with Psoriatic Arthritis
Notice bibliographique
Résumé
We compared treatment switch patterns and healthcare costs among biologic-naive patients with psoriatic arthritis (PsA) who initiated apremilast or biologics. A 1:2 propensity score match was used to adjust administrative claims data for adults initiating apremilast or biologics from January 1, 2014, to September 30, 2016, for possible selection bias. Patients had at least 12 months of pre- and post-index continuous enrollment in the Optum Clinformatics™ Data Mart database. Outcomes included switch frequency, days to switch, adherence on index treatment, and healthcare costs (total and per patient per month). Switch rate was defined as the proportion of patients who switched to a new treatment after initiation of the index treatment, and days to switch was calculated as the days between initiation of the index treatment and initiation of the new treatment. Adherence was calculated using the proportion of days covered and the medication possession ratio. The t test and chi-square, Kaplan–Meier, and Wilcoxon rank-sum tests were used to evaluate differences between the cohorts. Patient characteristics and switch rates were similar between the matched apremilast (n = 170) and biologic (n = 327) cohorts. After matching, patient characteristics were similar between the matched cohorts. The 12-month switch rates were similar for patients initiating apremilast versus those on biologics (17.7% vs. 25.1%, P = 0.06). This trend was similar at 6 months (7.7% vs. 13.2%, P = 0.07) and 18 months (24.4% vs. 29.3%, P = 0.33). Regardless of treatment switching, 12-month total healthcare costs were lower with apremilast versus biologics (all: $28,423 vs. $41,178, P < 0.0001; switched: $39,803 vs. $51,517, P = 0.0040; did not switch: $25,984 vs. $37,717, P < 0.0001). Biologic-naive patients with PsA who initiated apremilast had switch rates similar to biologic users and significantly lower healthcare costs, regardless of treatment switching. Psoriatic arthritis (PsA) is a chronic inflammatory disease that affects an estimated 30% of psoriasis patients who use systemic therapy. Symptoms of PsA, such as joint swelling and tenderness, can be painful and disabling and may worsen quality of life. PsA can also impart a substantial economic burden. Treatment for moderate to severe PsA often involves the use of systemic oral medications (e.g., conventional systemic treatments such as methotrexate or targeted systemic treatments such as apremilast) or biologic therapy given by injection or infusion. Because PsA symptoms and responses to treatment can vary, patients may switch treatments over time. More research is needed to better understand how switching treatments affects healthcare costs among patients starting treatment with apremilast or a biologic for PsA. This study compared treatment switching and healthcare costs among patients with PsA who had never been treated with a biologic and who started treatment with apremilast or a biologic for PsA. Rates of treatment switching at 12 months were similar for patients starting treatment with apremilast versus those starting a biologic. Patients starting treatment with apremilast had significantly lower total healthcare costs compared with those starting a biologic, even if they later switched to a biologic. Healthcare costs calculated per patient per month (PPPM) were also lower with apremilast versus biologics, driven by lower PPPM pharmacy costs. These findings suggest that starting treatment with apremilast may be an effective and cost-effective strategy for managing PsA, even for patients who later switch to a biologic.
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) | 0,000 | 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 ».