MétaCan
Menu
Retour à la cohorte
Enregistrement W4289060076

Flash Glucose Monitoring System for People with Type 1 or Type 2 Diabetes: A Health Technology Assessment.

2019· article· en· W4289060076 sur OpenAlexaboutno aff

Notice bibliographique

RevuePubMed · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueDiabetes Management and Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésType 2 diabetesFlash (photography)Continuous glucose monitoringMedicineType 1 diabetesDiabetes mellitusComputer scienceEndocrinologyArt
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: People with diabetes manage their condition by monitoring the amount of glucose (a type of sugar) in their blood, typically using a method called self-monitoring of blood glucose. Flash glucose monitoring is another method of assessing glucose levels; it uses a sensor placed under the skin and a separate touchscreen reader device. We conducted a health technology assessment of flash glucose monitoring for people with type 1 or type 2 diabetes, which included an evaluation of effectiveness and safety, the budget impact of publicly funding flash glucose monitoring, and patient preferences and values. METHODS: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Cochrane risk-of-bias tool for randomized controlled trials and the Cochrane ROBINS-I tool for nonrandomized studies, and we assessed the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search, and we analyzed the net budget impact of publicly funding flash glucose monitoring in Ontario for people with type 1 diabetes and for people with type 2 diabetes requiring intensive insulin therapy who are eligible for coverage under the Ontario Drug Benefit program. To contextualize the potential value of flash glucose monitoring, we spoke with adults with diabetes and parents of children with diabetes. RESULTS: Six publications met the eligibility criteria for the clinical evidence review. Compared with self-monitoring of blood glucose, people who used flash glucose monitoring spent on average 1 hour more in the target glucose range (95% confidence interval [CI] 0.41-1.59) and 0.37 hours (22 minutes) less in a high glucose range (95% CI -0.69 to -0.05) (GRADE: Moderate). Among adults with well-controlled type 1 diabetes, flash glucose monitoring was more effective than self-monitoring of blood glucose in reducing glucose variability (GRADE: Moderate). Flash glucose monitoring was more effective than self-monitoring of blood glucose in reducing the average time spent in hypoglycemia (-0.47 h [95% CI -0.73 to -0.21]) and the average number of hypoglycemia events (-0.16 [95% CI -0.29 to -0.03]) among adults with type 2 diabetes requiring intensive insulin therapy (GRADE: Moderate). Our certainty in the evidence for the effectiveness of flash glucose monitoring for other clinical outcomes, such as quality of life and severe hypoglycemia events, is low or very low. We identified no studies on flash glucose monitoring that included pregnant people, people with diabetes who did not use insulin, or children younger than 13 years of age.We identified two studies for the economic evidence review: one cost analysis and one cost-utility analysis. The cost analysis study, conducted from the perspective of United Kingdom's National Health Service, found that flash glucose monitoring reduced costs when self-monitoring of blood glucose was performed 10 times daily but was more expensive when self-monitoring of blood glucose was performed 5.6 times daily. The cost-utility analysis had methodological limitations and was not applicable to the context of Ontario's health care system.Our 5-year budget impact analysis found that flash glucose monitoring may lead to a net budget increase ranging from $14.6 million ($2.9 million for type 1 diabetes and $11.7 million for type 2 diabetes) in year 1, at an uptake rate of 15%, to $38.6 million ($7.7 million for type 1 diabetes and $30.9 million for type 2 diabetes) in year 5, at an uptake rate of 35%. In this analysis, we assumed that people with type 1 diabetes who self-monitor their blood glucose levels would perform six blood glucose tests daily and that people with type 2 diabetes would perform four blood glucose tests daily. For people switching from self-monitoring of blood glucose using the maximum number of blood glucose test strips for reimbursement (3,000 strips yearly) to flash glucose monitoring, the net budget impact of using flash glucose monitoring is likely to be small.Adults with diabetes and parents of children with diabetes with whom we spoke reported positively on their experiences with flash glucose monitoring, reporting they believed that flash glucose monitoring helped them control their blood glucose levels, resulting in physical, social, and emotional benefits. The cost of flash glucose monitoring was the largest barrier to its use. CONCLUSIONS: Based on an assessment of several glycemic outcomes, moderate-quality evidence shows that flash glucose monitoring improves diabetes management among adults with well-controlled type 1 diabetes and adults with type 2 diabetes requiring intensive insulin therapy. We estimate that publicly funding flash glucose monitoring in Ontario for people with type 1 diabetes and for people with type 2 diabetes requiring intensive insulin therapy who are eligible for coverage under the Ontario Drug Benefit program would result in additional costs of between $14.6 million and $38.6 million annually over the next 5 years. Adults with diabetes and parents of children with diabetes with whom we spoke reported that flash glucose monitoring helped them or their children control their blood glucose levels, resulting in physical, social, and emotional benefits.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,201
Score d'incertitude au seuil0,353

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,312
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2019
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePubMedMême sujetDiabetes Management and ResearchTravaux en français237 207