Advanced carbohydrate counting: An engineering perspective

Reiterer, Florian; Freckmann, Guido

doi:10.1016/j.arcontrol.2019.06.003

Cited by 13 publications

(16 citation statements)

References 103 publications

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“…These results offer initial evidence that both meal type and CHO amount are factors that influence the CHO counting error. The behavior of T1D subjects in the CHO estimation found in this work is in line with that obtained by two recent works by Reiterer et al 11 , 12 Moreover, the tendency to underestimate large meals, and to overestimate small meals, has also been detected in other works in the literature. 11–14 Lastly, some parameters describing the distribution of CHO counting errors for different levels of meal CHO amount in greater detail are reported in Table 1 .…”

Section: Resultssupporting

confidence: 93%

“…Other literature has investigated the impact of CHO counting errors on glycemic control, through both in vivo 15–17 and in silico 11 , 12 , 18 , 19 clinical trials and has shown that CHO counting errors can strongly influence postprandial BG excursions: CHO underestimation can cause postprandial hyperglycemia, whereas CHO overestimation can lead to hypoglycemic episodes.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Carbohydrate Counting Error in Type 1 Diabetes Management

Roversi

Vettoretti

Favero

et al. 2020

Diabetes Technology & Therapeutics

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Background: The error in estimating meal carbohydrates (CHO) amount is a critical mistake committed by type 1 diabetes (T1D) subjects. The aim of this study is both to investigate which factors, related to meals and subjects, affect the CHO-counting error most and, to develop a mathematical model of CHO-counting error embeddable in T1D patient decision simulators to do in silico clinical trials. Methods: A published dataset of 50 T1D adults is used, which includes patient's CHOcount of 692 meals, dietitian's estimates of meal composition (used as reference), and several potential explanatory factors. The CHO-counting error is modeled by multiple linear regression with stepwise variable selection starting from 10 candidate predictors, i.e. education level, insulin treatment duration, age, body weight, meal type, CHO, lipid, energy, protein and fiber content. Inclusion of quadratic and interaction terms is also evaluated. Results: Larger errors correspond to larger meals, most of the large meals are underestimated. The linear model selects CHO (p<0.00001), meal type (p<0.00001) and body weight (p=0.047), while its extended version embeds a quadratic term of CHO (p<0.00001) and interaction terms of meal type with CHO (p=0.0001) and fiber amount (p=0.001). The extended model explains 34.9% of the CHO-counting error variance. Comparison with the CHO-counting error description previously used in the T1D patient decision simulator shows that the proposed models return more credible realizations. Conclusions: The most important predictors of CHO-counting errors are CHO and meal type. The mathematical models proposed improve the description of patients' behavior in the T1D patient decision simulator.

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Section: Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Modeling Carbohydrate Counting Error in Type 1 Diabetes Management

Roversi

Vettoretti

Favero

et al. 2020

Diabetes Technology & Therapeutics

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“…Some studies attempted to quantify the impact of carbcounting error on T1D patients glycemic control, showing that CHO underestimation can cause postprandial hyperglycemia, while CHO overestimation can lead to hypoglycemic episodes. [16][17][18][19][20] Moreover, Smart et al 21 found that an error of ±10g per meal or snack on a meal size of 60g did not significantly deteriorate the postprandial control, while a ±20g error had a significant impact on postprandial glycemia. 22 Although errors in carb-counting can be mitigated by the adoption of closed-loop insulin delivery systems, 23 these systems are still used by only a fraction of people with T1D.…”

Section: Introductionmentioning

confidence: 99%

Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial

Roversi

Vettoretti

Favero

et al. 2021

J Diabetes Sci Technol

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Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

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“…The insulin boluses are delivered as peaks of rapid-acting insulin at mealtime, ideally 15-20 min earlier (Weinzimer et al 2008). To calculate the insulin boluses, patients must estimate the carbohydrate meal content (Reiterer et al 2019), which is a challenging and burdensome task.…”

Section: Scope and Motivationmentioning

confidence: 99%

“…where G(t 1 ) and G r (t 1 ) are the glucose value and the glucose set point at the detection time t 1 , respectively. The parameters ICR (g/U) and CF (mg/(dL U)) denote, respectively, the insulin-to-carbohydrate ratio and the correction factor of the standard open-loop therapy (Reiterer et al 2019). The parameter α refers to the equivalent meal bolus (Reiterer et al 2019).…”

Section: Bolusing Algorithmmentioning

confidence: 99%

New Contributions Towards Meal and Exercise Announcement-Free Artificial Pancreas Systems

Mira¹

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Advanced carbohydrate counting: An engineering perspective

Cited by 13 publications

References 103 publications

Modeling Carbohydrate Counting Error in Type 1 Diabetes Management

Modeling Carbohydrate Counting Error in Type 1 Diabetes Management

Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial

New Contributions Towards Meal and Exercise Announcement-Free Artificial Pancreas Systems

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