Cluster analysis of longitudinal treatment patterns in patients newly diagnosed with systemic lupus erythematosus in the United States
Notice bibliographique
Résumé
Treatments for systemic lupus erythematosus (SLE) include corticosteroids (CS), antimalarials, nonsteroidal anti-inflammatory drugs, cytotoxic agents, and immunosuppressive/immunomodulatory agents. We examined treatment patterns in newly diagnosed SLE patients from a multipayer US claims database. This study (GSK HO-13-13054) retrospectively followed incident SLE patients' treatment for 4 years in the MarketScan commercial claims database. The earliest medical claim date with SLE diagnosis (ICD-9 code 710.0x; 1 January 2002 to 31 March 2008) was the index date. Patients were ≥18 years at index, had continuous medical and pharmacy benefits for 12 months pre index without SLE diagnosis and 48 months post index, with ≥1 SLE-related inpatient claim or ≥2 office or emergency room visits with SLE diagnosis ≥30 days apart within 12 months post index. A specialist must have made ≥1 SLE diagnosis at index or within 12 months post index. Results were stratified by provider type (primary care physician (PCP)/specialist). A disjoint k-means cluster analysis identified treatment pathways using annual prescription numbers for CS, hydroxychloroquine (HCQ), mycophenolate mofetil, azathioprine, and methotrexate as input variables. The study identified 2,086 newly diagnosed SLE patients (mean age: 47.2 years; female: 91%). In the 4 years post index, 1,031 (49.4%) patients were not actively treated (<0.05 prescriptions/year). Of the 219 (10.5%) patients who primarily received CS, 42 had persistently high numbers of prescriptions (~1/month), and 177 received 4.9 (mean) prescriptions in Year 1, decreasing in Years 2 to 4. Three subgroups emerged within the 606 (29.1%) patients who primarily received HCQ: persistent high number of prescriptions (~1/month), persistent moderate number of prescriptions (3.2 to 4.1/year), and poor adherence (Year 1, 8.7 prescriptions; Years 2 to 4, decreasing prescriptions). Both CS and HCQ were received by 138 (6.6%) patients; 56 had high numbers of prescriptions (Years 1 to 4); 82 showed progressively decreasing prescriptions. Fifty-four (2.6%) and 38 (1.8%) patients had moderate numbers of prescriptions for methotrexate (5.4 to 8.4/year) and azathioprine (5.7 to 7.5/year), respectively, with some CS and HCQ prescriptions. Treatment patterns differed in patients seen by specialists versus PCPs ( P < 0.0001). Specialist-treated patients had a lower no-treatment rate than PCP-treated patients, and higher rates in active treatment clusters (Table 1 ). Treatment patterns were observed among SLE patients using medical resources. In the 4 years post diagnosis: ~50% of patients were not actively treated; 50% received CS, HCQ, and immunosuppressants with differing combinations, intensities, and adherence levels. Specialists provided more intensive treatment than PCPs.
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| 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 ».