SAT-625 Democratization of Glycemic Outcomes in People with Type 1 Diabetes (T1D) using the MiniMed™ 780G System of Automated Insulin Delivery (AID)
Notice bibliographique
Résumé
Abstract Disclosure: R.A. Vigersky: Medtronic Minimed. T. Cordero: Medtronic Diabetes. A. Arrieta: Medtronic Diabetes. M. Liu: Medtronic Diabetes. B. Grosman: Medtronic Diabetes. J. Shin: Medtronic Diabetes. Introduction: AID therapy is recognized as the standard of care for managing glycemia in children and adults with T1D by professional societies and health ministries, globally (ADA- 2025. doi:10.2337/dc25-S007; ISPAD- 2024. doi:10.1159/000543034; UK NICE- https://www.nice.org.uk/guidance/TA943). There are, currently, six commercialized AID therapies (MiniMed™ 780G [MM780G], CamAPS FX, Insulet Omnipod™ 5, Tandem Control™ IQ, Beta Bionics iLet™ and Diabeloop DBLG1 systems) in various countries each having different algorithmic approaches to managing glucose. Real-world data demonstrating the effectiveness of these systems comes from single centers, single countries, or multiple countries within a region. We hypothesized that the MM780G, an advanced hybrid closed-loop system with an algorithm that provides automated basal and correction insulin doses up to every 5 minutes and several glucose target (GT) and active insulin time (AIT) settings, could mitigate regional, cultural and dietary differences in glycemic outcomes in people with T1D and provide similar time in range (TIR, 70-180mg/dL), time above range (TAR 180mg/dL), time below range (TBR 70mg/dL) and glucose management indicator (GMI) and percentage of users reaching international consensus glycemic targets, across the globe. We also studied whether using the recommended optimal settings (ROS, 100mg/dL GT and 2hrs AIT ≥95% of the time) further narrows those differences. Methods: CareLink™ data of consenting MM780G users (any age, with ≥10 days of CGM use) that were uploaded since commercial availability in the following regions; Europe, Middle East and Africa (EMEA) (median 356 days of use), United States (US) (174.7 days), Asia-Pacific (APAC) (245 days), Latin America (LATAM) (213 days) and Canada (CAN) (261.9 days) were de-identified, aggregated and analyzed. The number of users in each region was >10,000 (>8,000 for CAN). Results: During overall settings use, mean %TIR and GMI were 72.1% and 6.9% (EMEA), 73.1% and 7.0% (US), 70.7% and 7.0% (APAC), and 74.0% and 6.8% (LATAM) and 72.6% and 7.0% (CAN). For all regions, mean %TAR was 23-27%, %TBR was ≤2.6% and 40-50% met consensus-recommended targets for all four metrics. In the 10-30% using the ROS, the %TIR and GMI were 77.6% and 6.8% in the EMEA, 78.0% and 6.8% in US, 76.3% and 6.8% in APAC, 78.1% and 6.7% in LATAM, and 78.3% and 6.7% in CAN. Mean %TAR was reduced to <21% and %TBR was ≤2.6% across regions. ROS use allowed 60-70% of users to meet targets. The trends in glycemic outcomes and rates meeting glycemic targets were observed for a majority of countries within the regions. Conclusion: The data show similar glycemic outcomes in real-world MM780G users across the world suggesting the algorithmic approach employed by the MM780G may be democratizing the management of T1D across cultures. The use of ROS increased the percentage reaching consensus-recommended targets by ∼20%. Presentation: Saturday, July 12, 2025
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 ».