Simulation software for assessment of nonlinear and adaptive multivariable control algorithms: Glucose–insulin dynamics in Type 1 diabetes

Rashid, Mudassir; Samadi, Sediqeh; Sevil, Mert; Hajizadeh, Iman; Kolodziej, Paul; Hobbs, Nicole; Maloney, Zacharie; Brandt, Rachel; Feng, Jun‐e; Park, Minsun; Quinn, Laurie; Çınar, Ali

doi:10.1016/j.compchemeng.2019.106565

Cited by 49 publications

(19 citation statements)

References 94 publications

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“…While a number of simulation tools effectively tackling various aspects of T1D pathophysiology 15 - 18 have been described in the literature, the mathematical description of aspects mainly related to patient behavior has, so far, been rarely investigated. 19 Nonetheless, lifestyle can remarkably affect the quality of glucose control in T1D management.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

Camerlingo

Vettoretti

Favero

et al. 2020

J Diabetes Sci Technol

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Background: In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al. Methods: Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed. Results: Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05). Conclusions: The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.

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Section: Introductionmentioning

confidence: 99%

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

Camerlingo

Vettoretti

Favero

et al. 2020

J Diabetes Sci Technol

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“…However, it is important to note that an FDA-approved simulator is currently unavailable for testing MAPS. In all, 2 groups have focused on developing their own multiple-input simulators [24,25], which would be beneficial for the progress of MAPS development. b Euglycemia target range 70-180 mg/dL (Jacobs et al [44] report euglycemia as 3.9-10 mmol/L, range 70.2-180 mg/dL, whereas all other studies report results for the range 70-180 mg/dL).…”

Section: Validation Methodologiesmentioning

confidence: 99%

“…A critical step toward AP advancement was the development of physiological models and simulators, which enabled the tuning and testing of different control algorithms in silico before conducting clinical studies, ensuring safety. The minimal model of glucose kinetics [20], the Sorenson model [21], the Hovorka model [22], the UVA/PADOVA simulator [23], the mGIPsim simulator [24], and the in silico patient population by Resalat et al [25] are some of the widely used models. The UVA/PADOVA simulator is currently the only FDA-approved simulator.…”

Section: Introductionmentioning

confidence: 99%

Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review

Hettiarachchi¹,

Daskalaki²,

Desborough³

et al. 2022

JMIR Diabetes

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Background Type 1 diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous subcutaneous infusion of insulin is a commonly used treatment method. Artificial pancreas systems (APS) use continuous glucose level monitoring and continuous subcutaneous infusion of insulin in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenging because of complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing and insulin action delays, and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating multi-input APS (MAPS), are being investigated. Objective The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D. Methods A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01%) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed. Results Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials. Conclusions The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life.

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“…In addition, dynamic trajectories for the glucose setpoint and constraints on the plasma insulin concentration (PIC) are integrated in the MPC algorithm to achieve better control of the BGC and reduce the risk of glycemic excursions. The performance of the proposed adaptive MPC strategy is compared with an MPC algorithm based on an autoregressive exogenous (ARX) model using the multivariable glucose-insulin-physiological variables simulator (mGIPsim) 22 where the effects of physical activity on glycemic dynamics are modeled and physiological variables such as energy expenditure are provided as simulator outputs for use in the in silico evaluation of multivariable glucose control system.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems

Sun

Rashid

Hobbs

et al. 2021

J Diabetes Sci Technol

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Background: Adaptive model predictive control (MPC) algorithms that recursively update the glucose prediction model are shown to be promising in the development of fully automated multivariable artificial pancreas systems. However, the recursively updated glycemic prediction models do not explicitly consider prior knowledge in the identification of the model parameters. Prior information of the glycemic effects of meals and physical activity can improve model accuracy and yield better glycemic control algorithms. Methods: A glucose prediction model based on regularized partial least squares (rPLS) method where the prior information is encoded as the regularization term is developed to provide accurate predictions of the future glucose concentrations. An adaptive MPC is developed that incorporates dynamic trajectories for the glucose setpoint and insulin dosing constraints based on the estimated plasma insulin concentration (PIC). The proposed adaptive MPC algorithm is robust to disturbances caused by unannounced meals and physical activities even in cases with missing glucose measurements. The effectiveness of the proposed adaptive MPC based on rPLS is investigated with in silico subjects of the multivariable glucose-insulin-physiological variables simulator (mGIPsim). Results: The efficacy of the proposed adaptive MPC strategy in regulating the blood glucose concentration (BGC) of people with T1DM is assessed using the average percent time in range (TIR) for glucose, defined as 70 to 180 mg/dL inclusive, and the average percent time in hypoglycemia (<70 and >54 mg/dL) and level 2 hypoglycemia (≤54 mg/dL). The TIR for a cohort of 20 virtual subjects of mGIPsim is 81.9% ± 7.4% (with no hypoglycemia or severe hypoglycemia) for the proposed MPC compared with 73.9% ± 7.6% (0.2% ± 0.1% in hypoglycemia and 0.1% ± 0.1% in level 2 hypoglycemia) for an MPC based on a recursive autoregressive exogenous (ARX) model. Conclusions: The adaptive MPC algorithm that incorporates prior knowledge in the recursive updating of the glucose prediction model can contribute to the development of fully automated artificial pancreas systems that can mitigate meal and physical activity disturbances.

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Simulation software for assessment of nonlinear and adaptive multivariable control algorithms: Glucose–insulin dynamics in Type 1 diabetes

Cited by 49 publications

References 94 publications

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review

Incorporating Prior Information in Adaptive Model Predictive Control for Multivariable Artificial Pancreas Systems

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