Gregory P. Forlenza, United States of AmericaUniversity of Colorado Denver Barbara Davis Center
Moderator Of 1 Session
Presenter Of 5 Presentations
FREQUENT BOLUSING IS ASSOCIATED WITH BETTER GLYCEMIC OUTCOMES IN 7,494 YOUTH WITH TYPE 1 DIABETES USING THE OMNIPOD INSULIN MANAGEMENT SYSTEM
Background and Aims
Higher bolus frequency is expected to correlate with better glycemic control; however, there is little real-world data quantifying this trend. This study retrospectively assessed glycemic outcomes stratified by bolus frequency for a large cohort of youth with T1D using the Omnipod® Insulin Management System (Insulet Corp., Acton, MA) with an integrated BG meter (Abbott Diabetes Care Inc., Alameda, CA) and data management system (Glooko, Mountain View, CA).
Insulin pump data uploaded to the data management system from February-August 2019 were matched via device serial number to a second database of self-reported demographic data and de-identified. Data from ≥3 mo of system use per user were analyzed. Glucose Management Indicator (GMI) and percentage of readings <54 and 70-180mg/dL were calculated based on ≥14 days of BG meter readings for users grouped by average bolus frequency (<3, 3-4.99, 5-7.99, or ≥8/day).
In 7,494 youth aged <18y with T1D (aged 12±4y, 50% female), average bolus frequency was 6.1±2.5/day, with 44% of users bolusing 5-7.99 times/day. Increased bolus frequency was correlated with improved GMI (Figure), decreased percentage of readings <54mg/dL, and increased percentage of readings 70-180mg/dL. The percentage of readings 70-180mg/dL increased from 32% with infrequent bolusing (<3/day) to 41% with frequent bolusing (≥8/day), while the percentage of readings <54mg/dL decreased from 2.5% to 1.2%.
Higher bolus frequency was associated with better glycemic control as measured by GMI and percentage of readings in target range in a large cohort of youth with T1D using the Omnipod System in this real-world observational study.
PERFORMANCE OF OMNIPOD PERSONALIZED MODEL PREDICTIVE CONTROL ALGORITHM WITH MULTIPLE SETPOINTS AND MEAL AND EXERCISE CHALLENGES IN ADULTS AND ADOLESCENTS WITH TYPE 1 DIABETES
- Gregory P. Forlenza, United States of America
- Bruce A. Buckingham, United States of America
- Jennifer Sherr, United States of America
- R. Paul Wadwa, United States of America
- Alfonso Galderisi, United States of America
- Laya Ekhlaspour, United States of America
- Cari Berget, United States of America
- Liana Hsu, United States of America
- Melinda Zgorski, United States of America
- Joon Bok Lee, United States of America
- Jason O’connor, United States of America
- Bonnie Dumais, United States of America
- Todd Vienneau, United States of America
- Lauren Huyett, United States of America
- Trang Ly, United States of America
Background and Aims
In preparation for future home studies, the safety and performance of the Omnipod hybrid closed-loop (HCL) personalized model predictive control (MPC) algorithm were assessed at multiple glucose setpoints and with meal and exercise challenges in adults and adolescents with type 1 diabetes (T1D) using an investigational device.
A 96-h HCL study was conducted in a supervised free-living hotel setting. Participants aged 12-85y with T1D and A1C<10.0% using CSII or MDI were eligible. At HCL start, the glucose setpoint was 150mg/dL, and was lowered to 120mg/dL after 48h. The system was stress-tested with 2 missed lunch boluses, high fat dinners, and daily moderate-intensity exercise. Endpoints were mean glucose and percentage time <54, <70, 70-180, >180, and ≥250mg/dL at each glucose setpoint. The 4-h glycemic response was assessed comparing a missed or 100% bolus for identical meals.
Participants (n=20) were (mean±SD): age 28.5±15.0, T1D duration 16.7±12.2y, and A1C 7.4±1.0%. Glycemic outcomes at each glucose setpoint and following the missed meal bolus challenge are reported in Tables A and B, respectively. Despite 2 missed meal bolus challenges (20-136g carbohydrate), percentage time from 70-180 mg/dL was 71.2±10.5% with a setpoint of 120mg/dL, and 63.2±10.0% with a setpoint of 150mg/dL. Mean glucose was 158±14mg/dL and 170±13mg/dL at the 120mg/dL and 150mg/dL setpoints, respectively.
The Omnipod personalized MPC algorithm performed well and was safe in adults and adolescents with T1D when stress-tested at 2 different setpoints under challenging conditions. These data support exploration of lower system setpoints, as well as future home-based trials.
Advances in Automated Insulin Delivery and Advanced Hybrid Closed Loops with the Dexcom G6
Omnipod® into the Future: Latest Clinical Findings
Clinical implementation and utilization of hybrid closed-loop technology
Background and Aims / Part 1
Background: Automated insulin delivery (AID) systems have the ability to dramatically improve glycemic control for patients with type 1 diabetes (T1D) while reducing burden and hypoglycemia. Early versions of these systems are hybrid closed loop (HCL) requiring user input of carbohydrate intake and often benefiting from user-initiated corrections and proper tuning. Such systems are thus not plug-and-play but rather complex medical aids for which users benefit from proper training and onboarding to optimize device use and benefit.
Methods / Part 2
Methods: I will review the published literature on HCL device training and clinical adoption of this technology across different clinical centers. I will also present the HCL implementation protocols from our large clinical center as well as those proposed for emerging HCL clinical trials.
Results / Part 3
Results: Multiple reports have demonstrated user attrition of HCL use with early generation devices. Increased burden has been postulated as the primary driver. Clinical trials on newer generation systems have indicated the potential for sustained use with improved device builds. Multiple system designs are emerging, each of which may require different forms of user training and onboarding.
Conclusions / Part 4
Discussion: New AID systems will continue to reduce device burden while improving overall glycemic control. Proper understanding of these devices is essential to drive appropriate patient training and system onboarding.