The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the pre-meal insulin bolus and compensate postprandial glucose excursion. However, CR is known to vary over time, within and between days, hence tracking its variations is important for optimizing glucose control. Physicians periodically tune this parameter, by trial and error, based on empirical guidelines and patient's diary, but contemporary diabetes technology has the potential to move to CR adaptation. The aim of this work is to propose an algorithm to adapt patient's CR to physiological and/or behavioral changes based on minimally-invasive everyday-life technology data. We developed a run-to-run (R2R) algorithm for CR adaptation exploiting a physiology-based method for CR optimization. The algorithm retrospectively evaluates the quality of glycemic control and proposes, every 2 days, an adaptation of patient's CR by using patient's minimally-invasive data. The performance of the algorithm was assessed in silico using the single-day University of Virginia/Padova T1D simulator (Visentin et al., J Diabetes Sci Technol 2018) which incorporates a model for intra-day variability of insulin sensitivity and dawn phenomenon. The feasibility and robustness of the algorithm was tested in a 35-day scenario (7 days of run-in), with 3 meals per day, in 100 in silico subjects by including inter-day variability of insulin sensitivity (Toffanin et al., IEEE Trans Biomed Eng 2018) together with suboptimal CR or basal insulin infusion rate. Different values of the R2R gain (lambda) were tested, ranging from pmb0 to 1. In all simulations, CR adaptation improves glycemic control in a significant percentage of virtual subjects, within 5 weeks. Moreover, the method was safe also in case of suboptimal insulin infusion rate. Based on simulation results, a good compromise between safety and efficacy was achieved with lambda between 0.5 and 1. The proposed R2R algorithm for CR adaptation proved to be effective in silico. These results need to be confirmed clinically. The method can potentially be used in conjunction with algorithms for basal insulin adaptation and/or closed-loop control.
Physiology-based run-to-run adaptation of insulin to carbohydrate ratio improves type 1 diabetes therapy: Results from an in silico study
Schiavon M.;Man C. D.;Cobelli C.
2019
Abstract
The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the pre-meal insulin bolus and compensate postprandial glucose excursion. However, CR is known to vary over time, within and between days, hence tracking its variations is important for optimizing glucose control. Physicians periodically tune this parameter, by trial and error, based on empirical guidelines and patient's diary, but contemporary diabetes technology has the potential to move to CR adaptation. The aim of this work is to propose an algorithm to adapt patient's CR to physiological and/or behavioral changes based on minimally-invasive everyday-life technology data. We developed a run-to-run (R2R) algorithm for CR adaptation exploiting a physiology-based method for CR optimization. The algorithm retrospectively evaluates the quality of glycemic control and proposes, every 2 days, an adaptation of patient's CR by using patient's minimally-invasive data. The performance of the algorithm was assessed in silico using the single-day University of Virginia/Padova T1D simulator (Visentin et al., J Diabetes Sci Technol 2018) which incorporates a model for intra-day variability of insulin sensitivity and dawn phenomenon. The feasibility and robustness of the algorithm was tested in a 35-day scenario (7 days of run-in), with 3 meals per day, in 100 in silico subjects by including inter-day variability of insulin sensitivity (Toffanin et al., IEEE Trans Biomed Eng 2018) together with suboptimal CR or basal insulin infusion rate. Different values of the R2R gain (lambda) were tested, ranging from pmb0 to 1. In all simulations, CR adaptation improves glycemic control in a significant percentage of virtual subjects, within 5 weeks. Moreover, the method was safe also in case of suboptimal insulin infusion rate. Based on simulation results, a good compromise between safety and efficacy was achieved with lambda between 0.5 and 1. The proposed R2R algorithm for CR adaptation proved to be effective in silico. These results need to be confirmed clinically. The method can potentially be used in conjunction with algorithms for basal insulin adaptation and/or closed-loop control.
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