Opioid utilization patterns among medicare patients with diabetic peripheral neuropathy.
Notice bibliographique
Résumé
BACKGROUND: Diabetic peripheral neuropathy (DPN) affects a large percentage of patients with type 2 diabetes and is associated with moderate-to-severe pain. Patients with DPN bear a substantial economic burden as a result of increased overall healthcare utilization. The reported costs of treating DPN are nearly $11 billion, with elderly (aged ≥65 years) patients with type 2 diabetes accounting for 93.1% ($10.2 billion) of the total costs. OBJECTIVES: To describe the real-world utilization patterns of long-acting opioids (LAOs) and chronic short-acting opioids (SAOs) use in a sample of Medicare enrollees (aged ≥65 years) with painful DPN, and to identify potential areas for improvement in the management of elderly patients with painful DPN who are treated with opioids. METHODS: In this retrospective pharmacy claims analysis, the Chronic Opioid Medication Use Evaluation (MUE) software was used to import and analyze individual plan, retrospective pharmacy utilization claims data from the MarketScan claims databases. Patients aged ≥65 years who had painful DPN as identified by ≥2 International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes for painful DPN (250.6X or 357.2) in at least 2 quarters in 2009, and who had ≥1 claims for LAO and/or chronic use of SAO (≥60 days of continuous therapy), were selected for analysis. Pharmacy claim data were extracted for 12 months, and various opioid utilization measures were reported. RESULTS: A total of 1448 unique Medicare patients with painful DPN were identified who had 11,740 claims for an LAO and/or chronic use of an SAO. Of the 1448 patients, 62% had chronic use of an SAO, and of these, 89% had no concurrent claim for LAO (minimum, 60-day overlap). The most frequently filled LAOs were fentanyl transdermal (38%), oxycodone controlled release (CR; 26%), and morphine CR/extended release (ER)/sustained release (SR; 20%). The daily average consumptions for fentanyl transdermal, oxycodone CR, and morphine CR/ER/SR were 0.3, 2.5, and 2.4, respectively. Among the study population, 15.2% of the patients filled an LAO or SAO prescription at ≥2 pharmacies. Furthermore, these elderly patients with painful DPN used greater doses of LAOs than what is recommended in the package insert, and 1.6% of patients used high doses of acetaminophen and 15.2% utilized multiple pharmacies to obtain their opioid prescriptions. Moreover, this population had prevalent concomitant use of opioids and prescribed gastrointestinal (GI) medications. CONCLUSION: Results from our retrospective pharmacy claims analysis demonstrated that elderly patients with painful DPN use doses of LAOs above those recommended in the package insert, with some patients using high doses of acetaminophen and utilizing multiple pharmacies to obtain their opioid prescriptions. In addition, this population had prevalent concomitant use of opioids and prescription GI medications. The use of software, such as the Opioid MUE, to monitor opioid drug utilization trends and examine other utilization measures can assist healthcare decision makers and payers in their utilization reviews to appropriately manage this population.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 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 ».