Ensemble Model Predictive Control Strategies Can Reduce Exercise Hypoglycemia in Type 1 Diabetes: In Silico Studies

García-Tirado, José; Colmegna, Patricio; Corbett, John P.; Özaslan, Başak; Breton, Marc D.

doi:10.23919/acc.2019.8814728

Cited by 9 publications

(10 citation statements)

References 30 publications

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“…The performance and safety of the proposed controller is compared to a baseline controller, the rMPC, published elsewhere. 40 The tuning parameters for both controllers are presented in Table 3. Figure 5 shows an individual closed-loop response under both MS-MPC and rMPC.…”

Section: Resultsmentioning

confidence: 99%

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

García-Tirado

Colmegna

Corbett

et al. 2019

J Diabetes Sci Technol

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Background: 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.

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Section: Resultsmentioning

confidence: 99%

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

García-Tirado

Colmegna

Corbett

et al. 2019

J Diabetes Sci Technol

Self Cite

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“…Different objectives can be easily considered in the optimization problem. A common choice is to minimize the square distance of BG from a glycemic reference, as done in [21] and [37]- [39] and in the Pavia/Padua MPC considered in this work [22], [23]. Other studies employed instead a zone-MPC strategy, penalizing BG levels predicted to fall outside of a target zone (the normoglycemic range) [13], [16]- [18].…”

Section: A Mpc For Apmentioning

confidence: 99%

Incorporating Sparse and Quantized Carbohydrates Suggestions in Model Predictive Control for Artificial Pancreas in Type 1 Diabetes

Pavan

Salvagnin

Facchinetti

et al. 2023

IEEE Trans. Contr. Syst. Technol.

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People with type 1 diabetes (T1D) face the challenge of administering exogenous insulin to maintain blood glucose (BG) levels in a safe physiological range, so as to avoid (possibly severe) complications. By automatizing insulin infusion, the artificial pancreas (AP) assists patients in this challenge. While insulin can decrease BG, having another input inducing glucose increase could further improve BG control. Here, we develop a model predictive control (MPC) algorithm that, in addition to insulin infusion, also provides suggestions of carbohydrates (CHOs) as a second, glucose-increasing, control input. Since CHO consumption has to be manually actuated, great care is paid in limiting the extra burden that may be caused to patients. By resorting to a mixed logical-dynamical MPC formulation, CHO intake is designed to be sparse in time and quantized. The algorithm is validated on the UVa/Padua T1D simulator, a well-established large-scale model of T1D metabolism, accepted by Food and Drug Administration (FDA). Compared with an insulin-only MPC, the new algorithm ensures increased time spent in the safe physiological range in 75% of patients. The improvement is limited for those already well controlled by the state-of-art strategy but relevant for the others: the 25th percentile of this metric is increased from 74.75% to 79.06% in the population. This is achieved while simultaneously decreasing time spent in hypoglycemia (from 0.5% to 0.12% in median) and with limited manual interventions (2.86 per day in median).

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“…Several simulation and clinical experiments conducted by our group have shown how anticipatory profiles integrated into AID systems can reduce the unwanted effects of glycemic disturbances. Simulation experiments have demonstrated that multistage model predictive controllers (MS-MPC) informed by disturbance profiles to anticipate moderate exercise’s effects can reduce hypoglycemia, 9 -11 results that were later confirmed in a randomized crossover clinical trial with 15 adult participants: 12 There were fewer hypoglycemic events (9 vs 33), and the percent time where BG was <70 mg/dL (time below range [TBR]) was 1.3% lower while the participants used the MS-MPC system compared with a well-tuned standard MPC. The overall reduction in hypoglycemia resulted in no significant increase in TAR.…”

Section: Introductionmentioning

confidence: 99%

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System

Corbett

García-Tirado

Colmegna

et al. 2021

J Diabetes Sci Technol

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Introduction: 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.

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Ensemble Model Predictive Control Strategies Can Reduce Exercise Hypoglycemia in Type 1 Diabetes: In Silico Studies

Cited by 9 publications

References 30 publications

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

Incorporating Sparse and Quantized Carbohydrates Suggestions in Model Predictive Control for Artificial Pancreas in Type 1 Diabetes

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System

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