A Peek Under the Hood: Explaining the MiniMed™ 780G Algorithm with Meal Detection Technology.

The MiniMed™ 780G system (780G) received Conformité Européenne mark in June 2020 and was, recently, approved by the U.S. Food and Drug Administration (April 2023). Clinical trials and real-world analyses have demonstrated MiniMed™ 780G system safety and effectiveness and that glycemic outcomes (i.e., time in range) improve with recommended settings use. In this publication, we will explain the iterative development of the 780G algorithm and how this technology has simplified diabetes management.

In Silico Evaluation of the Medtronic 780G System While Using the GS3 and Its Calibration-Free Successor, the G4S Sensor.

In silico simulation studies using 5807 virtual patients with insulin dependent diabetes have been conducted to estimate the risk and efficacy with the closed-loop 780G pump when switching between Medtronic Guardian Sensor 3 (GS3) and Medtronic Guardian 4 Sensor (G4S), next generation calibration free glucose sensor. To demonstrate by utilizing a case study that captures the merits of in silico studies with single hormone insulin dependent virtual patients that include variability in pharmacokinetics/pharmacodynamics, age, gender, insulin sensitivity and BMIs. Also, to show that in silico studies can uniquely isolate the effect of a single variable on clinical outcomes. Simulation studies results were compared to clinical and commercial data and were separated by age groups and pump settings. The commercial data, the clinical study data and the simulation studies predicted that switching between GS3 to G4S will introduce a change in glucose average, percentage time between 70 and 180 mg/dL, and percentage time below 70 mg/dL of: 5.2, 3.4, and 3.1 mg/dL, - 1.1, 0.2, and - 1.1%, and - 0.6, - 1.0, and - 0.3%, respectively. We demonstrated that our simulation studies were able to predict the difference in glycemic outcomes when switching between different sensors in real world setting, better than a small clinical controlled study. As predicted, switching between GS3 and G4S sensors with the 780G system does not introduce clinical risk and maintain the clinical outcomes of the sensor. We demonstrated the ability of insulin dependent diabetes virtual patients to predict clinical outcomes and to augment or even replace some small clinical studies.

Sensor-Augmented Pump-Based Customized Mathematical Model for Type 1 Diabetes.

BACKGROUND: Simulations using mathematical models are important for studying, developing, and improving therapies for people with type 1 diabetes. METHODS: The Medtronic CareLink® database was used to create virtual patients with a variety of inter-insulin sensitivities, meal absorption rates, pharmacokinetics, age, and gender. In addition, intra-insulin sensitivities of the virtual patients change over a 24-h cycle. RESULTS: A total of 2087 virtual patients were developed. The time percentage between 70 and 180 mg/dL of the CareLink uploads and the simulated virtual patients was 72.4% (18.6) and 74.1% (16.9), respectively. The time percentage <70 mg/dL of the real continuous glucose monitoring from CareLink uploads and the simulated virtual patients was 1% (2.4) and 1.7% (4.1), respectively. A simulation study with the virtual patients predicted the glycemic distribution after 2 h of insulin suspension as reported in the ASPIRE (Automation to Simulate Pancreatic Insulin Response) clinical trial. The 3 months outcomes of Medtronic's hybrid closed-loop 670G system pivotal trial were also predicted in a simulation study. The time percentage <70 mg/dL was 3.4% and 3.1%, and the time percentage between 71 and 180 mg/dL was 73.8% and 77.7% for 93 pivotal study adults (>18 years) and 90 adult (>28 years) virtual patients, respectively. CONCLUSION: The Medtronic CareLink database was utilized to generate a large number of virtual patients with a variety of insulin sensitivities, pharmacokinetics, and meal absorption rates. This new simulation model can be potentially used to evaluate and prognosticate the outcomes of studies of artificial pancreas algorithms and systems.