Continuous Monitoring of Glucose for Type 1 Diabetes: A Health Technology Assessment.
Notice bibliographique
Résumé
BACKGROUND: Type 1 diabetes is a condition in which the pancreas produces little or no insulin. People with type 1 diabetes must manage their blood glucose levels by monitoring the amount of glucose in their blood and administering appropriate amounts of insulin via injection or an insulin pump. Continuous glucose monitoring may be beneficial compared to self-monitoring of blood glucose using a blood glucose meter. It provides insight into a person's blood glucose levels on a continuous basis, and can identify whether blood glucose levels are trending up or down. METHODS: We conducted a health technology assessment, which included an evaluation of clinical benefit, value for money, and patient preferences related to continuous glucose monitoring. We compared continuous glucose monitoring with self-monitoring of blood glucose using a finger-prick and a blood glucose meter. We performed a systematic literature search for studies published since January 1, 2010. We created a Markov model projecting the lifetime horizon of adults with type 1 diabetes, and performed a budget impact analysis from the perspective of the health care payer. We also conducted interviews and focus group discussions with people who self-manage their type 1 diabetes or support the management of a child with type 1 diabetes. RESULTS: Twenty studies were included in the clinical evidence review. Compared with self-monitoring of blood glucose, continuous glucose monitoring improved the percentage of time patients spent in the target glycemic range by 9.6% (95% confidence interval 8.0-11.2) to 10.0% (95% confidence interval 6.75-13.25) and decreased the number of severe hypoglycemic events.Continuous glucose monitoring was associated with higher costs and small increases in health benefits (quality-adjusted life-years). Incremental cost-effectiveness ratios (ICERs) ranged from $592,206 to $1,108,812 per quality-adjusted life-year gained in analyses comparing four continuous glucose monitoring interventions to usual care. However, the uncertainty around the ICERs was large. The net budget impact of publicly funding continuous glucose monitoring assuming a 20% annual increase in adoption of continuous glucose monitoring would range from $8.5 million in year 1 to $16.2 million in year 5.Patient engagement surrounding the topic of continuous glucose monitoring was robust. Patients perceived that these devices provided important social, emotional, and medical and safety benefits in managing type 1 diabetes, especially in children. CONCLUSIONS: Continuous glucose monitoring was more effective than self-monitoring of blood glucose in managing type 1 diabetes for some outcomes, such as time spent in the target glucose range and time spent outside the target glucose range (moderate certainty in this evidence). We were less certain that continuous glucose monitoring would reduce the number of severe hypoglycemic events. Compared with self-monitoring of blood glucose, the costs of continuous glucose monitoring were higher, with only small increases in health benefits. Publicly funding continuous glucose monitoring for the type 1 diabetes population in Ontario would result in additional costs to the health system over the next 5 years. Adult patients and parents of children with type 1 diabetes reported very positive experiences with continuous glucose monitoring. The high ongoing cost of continuous glucose monitoring devices was seen as the greatest barrier to their widespread use.
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) | 0,000 | 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 ».