Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps. (June 2018)

• Record Type: Journal Article
• Authors: Rosales, Nicolás; De Battista, Hernán; Vehí, Josep; Garelli, Fabricio
• Is Part Of: Computer methods and programs in biomedicine. Volume 159(2018)
• Page Start: 145
• Page End: 158
• Publication Date: 2018-06
• Subjects: Diabetes management -- Glycemic control -- Insulin-On-Board -- Insulin dosing -- Duration of insulin action; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.03.007
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Automatic generation of a super bolus based on realistic two compartment insulin on board estimator. Easy to implement in current devices and low computational cost. In comparison with the standard basal-bolus treatment, the Automatic IOB-based Super Bolus may shield a better performance and a good postprandial control for high glycemic index meals. Abstract: Background and Objective: Although there has been significant progress towards closed-loop type 1 diabetes mellitus (T1DM) treatments, most diabetic patients still treat this metabolic disorder in an open-loop manner, based on insulin pump therapy (basal and bolus insulin infusion). This paper presents a method for automatic insulin bolus shaping based on insulin-on-board (IOB) as an alternative to conventional bolus dosing. Methods: The methodology presented allows the pump to generate the so-called super-bolus (SB) employing a two-compartment IOB dynamic model. The extra amount of insulin to boost the bolus and the basal cutoff time are computed using the duration of insulin action (DIA). In this way, the pump automatically re-establishes basal insulin when IOB reaches its basal level. Thus, detrimental transients caused by manual or a-priori computations are avoided. Results: The potential of this method is illustrated via in-silico trials over a 30 patients cohort in single meal and single day scenarios. In the first ones, improvements were found (standard treatment vs. automatic SB) both in percentage time in euglycemia (75 g meal: 81.9 ± 15.59 vs. 89.51 ± 11.95, ρ ≃ 0; 100 g meal: 75.12 ± 18.23 vs. 85.46 ± 14.96, ρ ≃ 0) and time in hypoglecymia (75 g meal: 5.92 ± 14.48 vs. 0.97 ± 4.15, ρ = 0.008 ; 100 g meal: 9.5 ± 17.02 vs. 1.85 ± 7.05, ρ = 0.014 ). In a single day scenario, considering intra-patient variability, the time in hypoglycemia was reduced (9.57 ± 14.48 vs. 4.21 ± 6.18, ρ = 0.028 ) and improved the time in euglycemia (79.46 ± 17.46 vs. 86.29 ± 11.73, ρ = 0.007 ). Conclusions: The automatic IOB-based SB has the potential of a better performance in comparison with the standard treatment, particularly for high glycemic index meals with high carbohydrate content. Both glucose excursion and time spent in hypoglycemia were reduced.