Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
J Diabetes Sci Technol. 2023 Nov;17(6):1470-1481. doi: 10.1177/19322968231206798. Epub 2023 Oct 20.
Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system.
The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range.
Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state.
The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.
模型预测控制（MPC）已成为 1 型糖尿病（T1D）自动胰岛素输送（AID）中最受欢迎的控制策略之一。这些算法依赖于预测模型来确定每个采样时间的最佳胰岛素剂量。虽然这些算法已经通过临床试验证明在血糖管理方面是安全有效的，但管理胰岛素输送和葡萄糖摄取之间不断变化的关系（即胰岛素敏感性，IS）仍然是一个挑战。我们旨在评估告知 AID 系统 IS 对系统性能的影响。
使用弗吉尼亚大学（UVA）基于 MPC 的混合闭环（HCL）和全闭环（FCL）系统。使用 UVA/帕多瓦 T1D 模拟器在不同 IS 水平下运行为期一天的模拟。通过混合餐葡萄糖耐量试验获得的估计 IS 值告知 AID 系统。更新相关控制器参数以告知 IS 的胰岛素剂量。使用惩罚目标血糖范围外时间的新性能指标评估 HCL/FCL 系统在有/无变化 IS 信息时的性能。
AID 系统中的反馈为 IS 变化提供了一定程度的容忍度。然而，根据个体的生理状态，IS 信息的推注和基础剂量改善了血糖结果，提供了对高血糖和低血糖的更大保护。
这里提出的概念验证分析表明，以准确估计 IS 的方式告知 AID 系统具有潜在的有益效果。特别是，在考虑降低 IS 的情况下，与盲目控制器相比，告知控制器提供了对高血糖的更大保护。同样，在 IS 增加的情况下，低血糖减少。需要进一步调整本文提出的适应方案，以克服在更具抵抗力的情况下观察到的增加的低血糖，并优化适应方法的性能。
