Impact of Carbohydrate Counting Errors on Glycemic Control in Type 1 Diabetes

Reiterer, Florian; Freckmann, Guido; Re, Luigi del

doi:10.1016/j.ifacol.2018.11.645

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…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 . It should be noted that increasing the amount of CHO results in a CHO counting error that becomes, on average, higher (in absolute value) and with a negative bias.…”

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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“…A bolus can then be provided, either automatically by the system or by the patient itself based on the estimated carbohydrate amount. This setup is highly prone to errors due to the difficulties of carbohydrate counting in everyday situations [14]. This difficulty is well established in the scientific literature, where the true effect of these errors is still a topic of debate.…”

Section: Introductionmentioning

confidence: 98%

“…On the other hand, Kawamura et al [17] found that meals with small amounts of carbohydrate tended to be overestimated. Finally, Reiterer et al [14] note that random errors, such as faulty carb-counting, as opposed to systematic bias errors, are more detrimental to glycemic control. Under-and over-bolusing due to these difficulties presents a significant risk of postprandial hyperglycemia and hypoglycemia.…”

Section: Introductionmentioning

confidence: 99%

In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

et al. 2020

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In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

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“…1 First-generation closed-loop systems have glucose-responsive automated basal insulin delivery; users are still required to estimate amounts of carbohydrate being consumed and enter these into their pump for prandial bolus insulin dose calculation. 2 However, many individuals with type 1 diabetes have difficulty accurately estimating carbohydrate content of meals 3 ; inaccurate carbohydrate counting is associated with higher post-prandial glucose, glucose variability and glycated haemoglobin (HbA 1c ). [4][5][6][7] Estimation errors >10 g carbohydrate have been shown to impact postprandial glucose.…”

mentioning

confidence: 99%