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

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 Delta TIR, Delta TAR, and Delta 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 (R-2>0.95), with slopes of beta(MEAN) = 0.21 beta(SD) = -0.07 for Delta TIR, beta(MEAN) = -0.25, beta(SD) = +0.06 for.TAR, and beta(MEAN) = 0.05, beta(SD) = +0.01 for Delta 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.

Automated summary

This study utilized in silico trials to quantify the impact of carb-counting errors on glycemic control in type 1 diabetes management. The results showed that random errors worsen glycemic control, while systematic underestimations lead to more time above the target range and systematic overestimations result in more time below the target range. Linear regression models were developed to mathematically describe the relationship between error mean, standard deviation, and changes in glycemic metrics.