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  • 1
    Online Resource
    Online Resource
    Adjusting Therapy Profiles When Switching to Ultra-Rapid Lispro in an Advanced Hybrid Closed-Loop System: An in Silico Study
    SAGE Publications ; 2022
    In:  Journal of Diabetes Science and Technology
    Details
    In: Journal of Diabetes Science and Technology, SAGE Publications
    Abstract: It has been shown that insulin acceleration by itself might not be sufficient to see clear improvements in glycemic metrics, and insulin therapy may need to be adjusted to fully leverage the extra safety margin provided by faster pharmacokinetic (PK) and pharmacodynamic (PD) profiles. The objective of this work is to explore how to perform such adjustments on a commercially available automated insulin delivery (AID) system. Methods: Ultra-rapid lispro (URLi) is modeled within the UVA/Padova simulation platform using data from previously published clamp studies. The Control-IQ AID algorithm is selected as it leverages carbohydrate-to-insulin ratio (CR in g/U), correction factor (CF in mg/dL/U), and basal rate (BR in U/h) daily profiles that are fully customizable. An experiment roadmap is proposed to understand how to safely modify these profiles when switching from lispro to URLi. Results: Simulations show that a 7% decrease in CR (approximately an 8% increase in prandial insulin) and a 7.5% increase in BR lead to cumulative improvements in glucose control with URLi. Comparing with baseline metrics using lispro, a clinically significant increase in time in the range of 70 to 180 mg/dL (overall: 70.2%-75.2%, P 〈 .001; 6 am-12 am: 62.4%-68.5%, P 〈 .001) and a reduction in time below 70 mg/dL (overall: 1.8%-1.2%, P 〈 .001; 6 am-12 am: 1.8%-1.3%, P 〈 .001) were observed. Conclusion: Properly adjusting therapy parameters allows to fully leverage glucose control benefits provided by faster insulin analogues, opening opportunities to take another step forward into a next generation of more effective AID solutions.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    URL: Article
    DOI: 10.1177/19322968221140401
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2467312-2
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  • 2
    Online Resource
    Online Resource
    Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes
    American Diabetes Association ; 2023
    In:  Diabetes Care Vol. 46, No. 9 ( 2023-09-01), p. 1652-1658
    Details
    In: Diabetes Care, American Diabetes Association, Vol. 46, No. 9 ( 2023-09-01), p. 1652-1658
    Abstract: Meals are a consistent challenge to glycemic control in type 1 diabetes (T1D). Our objective was to assess the glycemic impact of meal anticipation within a fully automated insulin delivery (AID) system among adults with T1D. RESEARCH DESIGN AND METHODS We report the results of a randomized crossover clinical trial comparing three modalities of AID systems: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+). Modalities were tested during three supervised 24-h admissions, where breakfast, lunch, and dinner were consumed per participant’s home schedule, at a fixed time, and with a 1.5-h delay, respectively. Primary outcome was the percent time in range 70–180 mg/dL (TIR) during the breakfast postprandial period for FCL+ versus FCL. RESULTS Thirty-five adults with T1D (age 44.5 ± 15.4 years; HbA1c 6.7 ± 0.9%; n = 23 women and n = 12 men) were randomly assigned. TIR for the 5-h period after breakfast was 75 ± 23%, 58 ± 21%, and 63 ± 19% for HCL, FCL, and FCL+, respectively, with no significant difference between FCL+ and FCL. For the 2 h before dinner, time below range (TBR) was similar for FCL and FCL+. For the 5-h period after dinner, TIR was similar for FCL+ and FCL (71 ± 34% vs. 72 ± 29%; P = 1.0), whereas TBR was reduced in FCL+ (median 0% [0–0%] vs. 0% [0–0.8%] ; P = 0.03). Overall, 24-h control for HCL, FCL, and FCL+ was 86 ± 10%, 77 ± 11%, and 77 ± 12%, respectively. CONCLUSIONS Although postprandial control remained optimal with hybrid AID, both fully AID solutions offered overall TIR & gt;70% with similar or lower exposure to hypoglycemia. Anticipation did not significantly improve postprandial control in AID systems but also did not increase hypoglycemic risk when meals were delayed.
    Type of Medium: Online Resource
    ISSN: 0149-5992 , 1935-5548
    URL: Article
    DOI: 10.2337/dc23-0119
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2023
    detail.hit.zdb_id: 1490520-6
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  • 3
    Online Resource
    Online Resource
    Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes
    SAGE Publications ; 2021
    In:  Journal of Diabetes Science and Technology Vol. 15, No. 6 ( 2021-11), p. 1326-1336
    Details
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 15, No. 6 ( 2021-11), p. 1326-1336
    Abstract: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability. Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA). Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD 〈 10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions. Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    URL: Article
    DOI: 10.1177/1932296820973193
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2467312-2
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  • 4
    Online Resource
    Online Resource
    Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes
    Mary Ann Liebert Inc ; 2021
    In:  Diabetes Technology & Therapeutics Vol. 23, No. 4 ( 2021-04-01), p. 277-285
    Details
    In: Diabetes Technology & Therapeutics, Mary Ann Liebert Inc, Vol. 23, No. 4 ( 2021-04-01), p. 277-285
    Type of Medium: Online Resource
    ISSN: 1520-9156 , 1557-8593
    URL: Article
    DOI: 10.1089/dia.2020.0516
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2021
    detail.hit.zdb_id: 2004914-6
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  • 5
    Online Resource
    Online Resource
    Anticipating Meals with Behavioral Profiles in an Artificial Pancreas System - An Informed Multistage Model Predictive Control Approach
    Elsevier BV ; 2020
    In:  IFAC-PapersOnLine Vol. 53, No. 2 ( 2020), p. 16305-16310
    Details
    In: IFAC-PapersOnLine, Elsevier BV, Vol. 53, No. 2 ( 2020), p. 16305-16310
    Type of Medium: Online Resource
    ISSN: 2405-8963
    URL: Article
    DOI: 10.1016/j.ifacol.2020.12.652
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2839185-8
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  • 6
    Online Resource
    Online Resource
    Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination
    SAGE Publications ; 2021
    In:  Journal of Diabetes Science and Technology Vol. 15, No. 4 ( 2021-07), p. 833-841
    Details
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 15, No. 4 ( 2021-07), p. 833-841
    Abstract: Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study. Methods: Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller’s aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller’s aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention. Results: Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P 〈 .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] ( P 〈 .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] ( P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] ( P 〈 .05) for α = 3. Conclusion: These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    URL: Article
    DOI: 10.1177/1932296820928067
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2467312-2
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  • 7
    Online Resource
    Online Resource
    In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System
    SAGE Publications ; 2019
    In:  Journal of Diabetes Science and Technology Vol. 13, No. 6 ( 2019-11), p. 1054-1064
    Details
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 13, No. 6 ( 2019-11), p. 1054-1064
    Abstract: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. Methods: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. Results: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). Conclusion: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    URL: Article
    DOI: 10.1177/1932296819879084
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2467312-2
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  • 8
    Online Resource
    Online Resource
    Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System
    SAGE Publications ; 2022
    In:  Journal of Diabetes Science and Technology Vol. 16, No. 1 ( 2022-01), p. 52-60
    Details
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 16, No. 1 ( 2022-01), p. 52-60
    Abstract: Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control. Methods: A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient’s total daily insulin (TDI) modulated by the disturbance’s likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module. Results: Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL 〈 0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%). Conclusions: The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    URL: Article
    DOI: 10.1177/19322968211059159
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2467312-2
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  • 9
    Online Resource
    Online Resource
    Mapping data to virtual patients in type 1 diabetes
    Elsevier BV ; 2020
    In:  Control Engineering Practice Vol. 103 ( 2020-10), p. 104605-
    Details
    In: Control Engineering Practice, Elsevier BV, Vol. 103 ( 2020-10), p. 104605-
    Type of Medium: Online Resource
    ISSN: 0967-0661
    URL: Article
    DOI: 10.1016/j.conengprac.2020.104605
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 1501351-0
    detail.hit.zdb_id: 1150140-6
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  • 10
    Online Resource
    Online Resource
    Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system
    Elsevier BV ; 2021
    In:  Computer Methods and Programs in Biomedicine Vol. 211 ( 2021-11), p. 106401-
    Details
    In: Computer Methods and Programs in Biomedicine, Elsevier BV, Vol. 211 ( 2021-11), p. 106401-
    Type of Medium: Online Resource
    ISSN: 0169-2607
    URL: Article
    DOI: 10.1016/j.cmpb.2021.106401
    RVK:
    XA 37224
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 1466281-4
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