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

Abstract

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.

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems

“…This baseline system is represented as HAID SBC MCHO. The CHO misestimation error is incorporated as a Gaussian distribution according to the recently published methodology 41 . To implement the SBC, the parameters required are the carbohydrate-to-insulin ratio (CR) and correction factor (CF), calculated based on clinical guidelines 42 .…”

ISPAD Clinical Practice Consensus Guidelines 2022: Nutritional management in children and adolescents with diabetes

“…Accuracy and consistency in carbohydrate counting are important to optimize postprandial glycemia and reduce glucose variability 126,127 . There is no universal definition for accuracy of carbohydrate counting.…”

“…125 Accuracy and consistency in carbohydrate counting are important to optimize postprandial glycemia and reduce glucose variability. 126,127 There is no universal definition for accuracy of carbohydrate counting. Research has shown that children, adolescents and their parents can carbohydrate count with a degree of accuracy, however under and over-estimation of foods remains a challenge.…”

Development and Theoretical Underpinnings of the PRIORITY Intervention: A Parenting Intervention to Prevent Disordered Eating in Children and Young People With Type 1 Diabetes

“…Combined with the fact that CYP are already managing the diagnosis of a life-long health condition and having to develop complex self-management and behavioural skills, it is not surprising that T1DE can develop. The focus on carbohydrate counting, whilst beneficial to attain euglycaemia ( 10 ) can lead to several psychological difficulties with food, impacting both individual and family dynamics. Foods containing carbohydrate can start to be perceived as ‘bad’ and thus avoided or prohibited within families.…”