Continuous glucose monitoring for self-management of diabetes in people living with type 2 diabetes mellitus on basal insulin therapy: A microsimulation model and cost-effectiveness analysis from a US perspective with relevance to Medicaid
Notice bibliographique
Résumé
BACKGROUND: Reducing the risks of complications is a primary goal of diabetes management, with effective glycemic control a key factor. Glucose monitoring using continuous glucose monitoring (CGM) technology is an important part of diabetes self-management, helping patients reach and maintain targeted glucose and glycated hemoglobin (HbA1c) levels. Although clinical guidelines recommended CGM use, coverage by Medicaid is limited, likely because of cost concerns. OBJECTIVE: To assess the cost-effectiveness of FreeStyle Libre CGM systems, compared with capillary-based self-monitoring of blood glucose (SMBG), in US individuals with type 2 diabetes mellitus using basal insulin. METHODS: A patient-level microsimulation model was used to compare CGM with SMBG for a population of 10,000 patients. A 10-year horizon was used, with an annual discount rate of 3.0% for costs and utilities. Model population characteristics were based on US national epidemiology data. Patient outcomes were based on published clinical trials and real-world studies. Annual costs, reflective of 2023 values, included CGM and SMBG acquisition costs and the costs of treating diabetic ketoacidosis, severe hypoglycemia, and diabetes complications. The effect of CGM was modeled as a persistent 1.1% reduction in HbA1c relative to SMBG based on US real-world evidence. Disutilities were based on published clinical trials and other relevant literature. The primary outcome was cost per quality-adjusted life-year (QALY) gained. Sensitivity analyses were performed to test the validity of the model results when accounting for a plausible variation of inputs. RESULTS: In the base case analysis, CGM was dominant to SMBG, providing more QALYs (6.18 vs 5.97) at a lower cost ($70,137 vs $71,809) over the 10-year time horizon. A $10,456 increase in glucose monitoring costs was offset by a $12,127 reduction in treatment costs. Cost savings reflected avoidance of acute diabetic events (savings owing to reductions in severe hypoglycemia and diabetic ketoacidosis were $271 and $2,159, respectively) and a reduced cumulative incidence of diabetes complications, particularly renal failure (saving $5,292), myocardial infarction (saving $1,996), and congestive heart failure (saving $1,061). Scenario analyses were consistent with the base case results, and the incremental cost-effectiveness ratio for CGM vs SMBG ranged from dominant to cost-effective. In probabilistic analysis, CGM was 100% likely to be cost-effective at a willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: CGM is cost-effective compared with SMBG for US patients with type 2 diabetes mellitus receiving basal insulin therapy. This suggests that state Medicaid programs could benefit from broader coverage of CGM.
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 | 0,001 | 0,001 |
| 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 ».