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

Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management

In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called "do-it-yourself" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.

Validation of the UVA Simulation Replay Methodology Using Clinical Data: Reproducing a Randomized Clinical Trial

Background: Computer simulators of human metabolism are powerful tools to design and validate new diabetes treatments. However, these platforms are often limited in the diversity of behaviors and glycemic conditions they can reproduce. Replay methodologies leverage field-collected data to create ad hoc simulation environments representative of real-life conditions. After formal validations of our method in prior publications, we demonstrate its capacity to reproduce a recent clinical trial. Methods: Using the replay methodology, an ensemble of replay simulators was generated using data from a randomized crossover clinical trial comparing the hybrid closed loop (HCL) and fully closed loop (FCL) control modalities in automated insulin delivery (AID), creating 64 subject/modality pairs. Each virtual subject was exposed to the alternate AID modality to compare the simulated versus observed glycemic outcomes. Equivalence tests were performed for time in, below, and above range (TIR, TBR, and TAR) and high and low blood glucose indices (HBGI and LBGI) considering equivalence margins corresponding to clinical significance. Results: TIR, TAR, LBGI, and HBGI showed statistical and clinical equivalence between the original and the simulated data; TBR failed the equivalence test. For example, in the HCL mode, simulated TIR was 84.89% versus an observed 84.31% (P = 0.0170, confidence interval [CI] [-3.96, 2.79]), and for FCL mode, TIR was 76.58% versus 77.41% (P = 0.0222, CI [-2.54, 4.20]). Conclusion: Clinical trial data confirm the prior in silico validation of the UVA replay method in predicting the glycemic impact of modified insulin treatments. This in vivo demonstration justifies the application of the replay method to the personalization and adaptation of treatment strategies in people with type 1 diabetes.

Use of a decision support tool and quick start onboarding tool in individuals with type 1 diabetes using advanced automated insulin delivery: a single-arm multi-phase intervention study

BACKGROUND

Multiple clinician adjustable parameters impact upon glycemia in people with type 1 diabetes (T1D) using Medtronic Mini Med 780G (MM780G) AHCL. These include glucose targets, carbohydrate ratios (CR), and active insulin time (AIT). Algorithm-based decision support advising upon potential settings adjustments may enhance clinical decision-making.

METHODS

Single-arm, two-phase exploratory study developing decision support to commence and sustain AHCL. Participants commenced investigational MM780G, then 8 weeks Phase 1-initial optimization tool evaluation, involving algorithm-based decision support with weekly AIT and CR recommendations. Clinicians approved or rejected CR and AIT recommendations based on perceived safety per protocol. Co-design resulted in a refined algorithm evaluated in a further identically configured Phase 2. Phase 2 participants also transitioned to commercial MM780G following "Quick Start" (algorithm-derived tool determining initial AHCL settings using daily insulin dose and weight). We assessed efficacy, safety, and acceptability of decision support using glycemic metrics, and the proportion of accepted CR and AIT settings per phase.

RESULTS

Fifty three participants commenced Phase 1 (mean age 24.4; Hba1c 61.5mmol/7.7%). The proportion of CR and AIT accepted by clinicians increased between Phases 1 and 2 respectively: CR 89.2% vs. 98.6%, p < 0.01; AIT 95.2% vs. 99.3%, p < 0.01. Between Phases, mean glucose percentage time < 3.9mmol (< 70mg/dl) reduced (2.1% vs. 1.4%, p = 0.04); change in mean TIR 3.9-10mmol/L (70-180mg/dl) was not statistically significant: 72.9% ± 7.8 and 73.5% ± 8.6. Quick start resulted in stable TIR, and glycemic metrics compared to international guidelines.

CONCLUSION

The co-designed decision support tools were able to deliver safe and effective therapy. They can potentially reduce the burden of diabetes management related decision making for both health care practitioners and patients.

TRIAL REGISTRATION

Prospectively registered with Australia/New Zealand Clinical Trials Registry(ANZCTR) on 30th March 2021 as study ACTRN12621000360819.

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.

Fast-acting insulin aspart (Fiasp®) improves glycemic outcomes when used with MiniMedTM 670G hybrid closed-loop system in simulated trials compared to NovoLog®

INTRODUCTION

Medtronic has developed a virtual patient simulator for modeling and predicting insulin therapy outcomes for people with type 1 diabetes (T1D). An enhanced simulator was created to estimate outcomes when using the MiniMed^TM 670G system with standard NovoLog® (EU: NovoRapid, US: NovoLog) versus Fiasp ® by using clinical data.

METHODS

Sixty-seven participants' PK profiles were generated per type of insulin (Total of 134 PK profiles). 7,485 virtual patients' PK measurements was matched with one of the 67 NovoLog® PK Tmax values. These 7,485 virtual patients were then simulated using the Medtronic MiniMed™ 670G algorithm following an in-silico protocol of 90 days: 14 days in open loop (Manual Mode) followed by 76 days in closed loop (Auto Mode). Simulation study was repeated with each NovoLog® PK profile being replaced by its corresponding Fiasp® PK profile in the same virtual patient. To validate our in-silico analysis, we compared the results of "actual" 19 "real life" patients from a clinical study RESULTS: Simulated overall and postprandial glycemic outcomes improved in all age groups with Fiasp®. The percentage of time in the euglycemic range increased by about ~2.2% with Fiasp®, in all age groups (p < 0.01). The percentage of time spent at <70 mg/dL was reduced by about ~0.6% with insulin Fiasp® (p < 0.01) and the mean glucose was reduced by about 1.3 mg/dL (p < 0.01), excluding those age <7 years. The simulated mean postprandial SG was reduced by ~5 mg/dL, a significant difference for all age groups. A clinical study results showed similar improvements with MiniMed^TM 670G system when switching from NovoLog® to Fiasp®.

CONCLUSIONS

The simulation studies indicate that using Fiasp® in place of NovoLog® with the MiniMed^TM 670G system will significantly improve the time spent in the healthy, euglycemic range and reduce exposure to hyperglycemia and hypoglycemia in most patients.

Offset-free MPC strategy for nonzero regulation of linear impulsive systems

In various biomedical applications, drug administration treatment can be modeled as an impulsive control system. Despite the development of different control strategies for impulsive systems, the elimination of the offset generated by a plant-model mismatch has not yet been researched. In biomedical systems, this mismatch is a consequence of physiological changes and can result in inaccurate treatment of patients. Therefore, control techniques that accomplish the objectives by compensating the effect of variations are required. The present paper proposes and substantiates a novel offset-free model predictive control (MPC) strategy for impulsive systems. To that aim, an impulsive disturbance model is introduced, and an observer design is developed that includes new observability criteria for estimating the disturbance and the state. Further, it is demonstrated that the proposed control strategy achieves zero offset tracking from an analysis of the observer and the controller at steady state. Additionally, the controller incorporates a recent MPC formulation to steer the state to an equilibrium set using artificial/intermediary variables to achieve nonzero regulation. Finally, these results are evaluated and illustrated using a dynamical model for type 1 diabetic patients.

Glucose Control in Adults with Type 1 Diabetes Using a Medtronic Prototype Enhanced-Hybrid Closed-Loop System: A Feasibility Study

Background: Experience from first-generation closed-loop (CL) systems informs refinements to enhance glucose control and user acceptance. A next-generation prototype enhanced-hybrid CL (E-HCL) system incorporates iterative changes to the Medtronic MiniMed 670G CL system, including automated correction boluses, lower target glucose level, and user enhancements. The aim was to explore safety, system performance, and glucose control using E-HCL in adults with type 1 diabetes. Methods: Twelve adults underwent this first in-human feasibility study. After a 1-week run-in using open-loop (OL), E-HCL was activated at the start of a supervised 1-week hotel phase, followed by 3 weeks free living at home. Supervised challenges included two meal interventions (unannounced and late meal bolus) and a sensor calibration intervention. Primary outcome was sensor glucose time-in-range (TIR); OL run-in and E-HCL at home were compared by Wilcoxon signed-rank test. Results: Twelve adults (seven men; median [interquartile range] age 48 [39, 57] years; HbA_1c 6.8 [6.2, 7.2]%, 51 [44, 55] mmol/mol; diabetes duration 31 [13, 41] years) completed the protocol. E-HCL resulted in greater TIR (85.3 [79.4, 88.4]% vs. 75.0 [66.6, 83.7]%, P = 0.003) and lower mean sensor glucose (123.0 [119.3, 129.6] mg/dL vs. 143.5 [135.8, 154.5] mg/dL, P = 0.002) than OL. Time spent <70 mg/dL increased using E-HCL (4.4 [3.3, 6.1]% vs. 3.0 [1.8, 3.8]%, P = 0.02) with no difference in time <54 mg/dL (P = 0.64). Time in CL was 99.98 [99.0, 100.0]%. All participants were satisfied using E-HCL. Conclusions: In adults with well-controlled HbA_1c levels, a prototype E-HCL resulted in high TIR, few CL exits, and positive user experiences at the expense of increased hypoglycemia (<70 mg/dL). E-HCL represents a positive step in the journey toward optimizing glucose control in people living with type 1 diabetes.