Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET. Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina.
Grupo de Control Aplicado (GCA), Instituto LEICI, UNLP-CONICET. Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina.
Comput Methods Programs Biomed. 2018 Jun;159:145-158. doi: 10.1016/j.cmpb.2018.03.007. Epub 2018 Mar 9.
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.
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.
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 (75g meal: 81.9 ± 15.59 vs. 89.51 ± 11.95, ρ ≃ 0; 100g meal: 75.12 ± 18.23 vs. 85.46 ± 14.96, ρ ≃ 0) and time in hypoglecymia (75g meal: 5.92 ± 14.48 vs. 0.97 ± 4.15, ρ=0.008; 100g 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).
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.
尽管在闭环 1 型糖尿病(T1DM)治疗方面取得了重大进展,但大多数糖尿病患者仍以开环方式治疗这种代谢紊乱,基于胰岛素泵治疗(基础和推注胰岛素输注)。本文提出了一种基于胰岛素输注量(IOB)的自动胰岛素推注成型方法,作为传统推注剂量的替代方法。
本文提出的方法允许泵使用双室 IOB 动力学模型生成所谓的超推注(SB)。使用胰岛素作用持续时间(DIA)计算额外的胰岛素剂量以增强推注和基础截止时间。通过这种方式,当 IOB 达到基础水平时,泵会自动重新建立基础胰岛素。因此,避免了手动或先验计算引起的有害瞬变。
通过在单餐和单日内对 30 名患者队列的模拟试验,说明了该方法的潜力。在第一个试验中,无论是在正常血糖时间百分比(75g 餐:81.9±15.59 与 89.51±11.95,ρ≃0;100g 餐:75.12±18.23 与 85.46±14.96,ρ≃0)还是低血糖时间百分比(75g 餐:5.92±14.48 与 0.97±4.15,ρ=0.008;100g 餐:9.5±17.02 与 1.85±7.05,ρ=0.014)都有改善。在单日内的情况下,考虑到个体内变异性,低血糖时间减少(9.57±14.48 与 4.21±6.18,ρ=0.028),正常血糖时间增加(79.46±17.46 与 86.29±11.73,ρ=0.007)。
与标准治疗相比,基于自动 IOB 的 SB 具有更好的性能潜力,特别是对于高血糖指数、高碳水化合物含量的膳食。血糖波动和低血糖时间均减少。