Real-time insulin bolusing for unannounced meals with artificial pancreas

Turksoy, Kamuran; Hajizadeh, Iman; Samadi, Sediqeh; Feng, Jun‐e; Sevil, Mert; Park, Minsun; Quinn, Laurie; Littlejohn, Elizabeth; Çınar, Ali

doi:10.1016/j.conengprac.2016.08.001

Cited by 48 publications

(31 citation statements)

References 33 publications

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“…20 An unscented Kalman filter estimates the glucose rate of appearance in an extended Bergman's minimal model, 21 and the glucose regulation as the result of the suggested multi-step insulin dosing for the detected meal is compared with the no-meal announcement case. 22 A variable state dimension approach is used to detect meals and to estimate the meal size by using a Kalman filter that switches its operation between two models. 23 A physiological parameter-invariant meal detector was proposed by defining a design score equivalent to the confidence level in the occurrence of meal based on the minimal glucose-insulin model.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

Samadi

Rashid

Turksoy

et al. 2018

Diabetes Technology & Therapeutics

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Section: Introductionmentioning

confidence: 99%

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

Samadi

Rashid

Turksoy

et al. 2018

Diabetes Technology & Therapeutics

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“…Due to this difference in dynamics of insulin action and carbohydrate (CHO) absorption, attempts to avoid hyperglycemic peaks are usually accompanied by hypoglycemic excursions [10]. Several approaches have been tested to overcome such issue [11][12][13][14], but while an ultrarapid insulin analogue is not available [15], postprandial control using subcutaneous route will continue to be a challenging situation for closed-loop systems. Diverse studies have included estimations of insulin concentration in the body to avoid excessive insulin stacking [16][17][18][19]; however, hypo-and hyperglycemia are still a hazard for AP systems, and novel approaches are still required.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Bertachi

Biagi

Beneyto

et al. 2020

Journal of Healthcare Engineering

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The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

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“…Despite achieving good results, there is still an unavoidable compromise between prandial hyperglycemia and postprandial hypoglycemia, mainly due to the slow pharmacokinetics and pharmacodynamics of the current insulin analogs. In Turksoy et al, 26 this compromise was reduced since meals were compensated with an additional module that delivered insulin boluses when an intake was detected. Also, clinical studies involving closed-loop control in patients with noncritical care have been carried out.…”

Section: Introductionmentioning

confidence: 99%

Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement

Fushimi

Colmegna

Battista

et al. 2019

J Diabetes Sci Technol

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Background: Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses—the so-called automatic regulation of glucose (ARG)—was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. Method: An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. Results: The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). Conclusion: In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.

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Real-time insulin bolusing for unannounced meals with artificial pancreas

Cited by 48 publications

References 33 publications

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement

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