Advanced Hypoglycemia Prevention Capabilities in Fuzzy Logic Artificial Pancreas

模糊逻辑人工胰腺先进的低血糖预防功能

• 批准号: 8195270
• 负责人: Richard Scott Mauseth
• 金额: $ 37.81万
• 依托单位: DOSE SAFETY COMPANY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-25 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8195270
• 关键词: Address; Algorithms; Area; Artificial Pancreas; Blood Glucose; Clinical; Clinical Data; Clinical Trials; Computer Simulation; Computer software; Data; Development; Devices; Diabetes Mellitus; Dose; Educational workshop; Environment; Funding; Fuzzy Logic; Glucose; Goals; Grant; Hour; Human; Hypoglycemia; Insulin; Insulin-Dependent Diabetes Mellitus; Lead; Manufacturer Name; Medical Device; Metabolic Control; Methods; Modeling; Patients; Performance; Physiological; Prevention; Publishing; Research; Safety; Schedule; Simulate; System; Technology; Testing; Translating; United States National Institutes of Health; Validation; Variant; Work; base; blood glucose regulation; commercial application; commercialization; design; glycemic control; improved; insulin sensitivity; meetings; novel; novel strategies; predictive modeling; prevent; prototype; research clinical testing; sensor; technological innovation

DESCRIPTION (provided by applicant): The ultimate technical objective of this research is the development of an Artificial Pancreas (AP) controller that improves metabolic control and decreases glycemic excursions by robustly preventing hypoglycemic episodes. Proportional-integral-derivative control (PID) and model predictive control (MPC) have been widely considered to be promising candidate for glucose control. However, PID and MPC methods are dependent on models, and a good model for T1DM is not easy to develop because of a number of physiological limitations, e.g., unmeasured meal size and frequent insulin sensitivity variations. Model-free approaches, such as fuzzy logic (FL) control offer a different and promising direction for improved glycemic control. A further benefit of FL is the low computational needs it requires compared to traditional controllers, which lead to smaller CPUs, lower power requirements and smaller batteries, all of which lead to a more usable AP system. This research seeks to improve the blood sugar control and hypoglycemia prevention capabilities of the controller used in our 2010-11 JDRF-funded clinical trial. Specifically, Part 1 of this research will use the UVA simulator to evaluate four specific potential improvements to our present FL controller: 1), a Low Glucose Suspend (LGS) feature employing reactive and predictive algorithms; 2) novel controller personalization and online adaptation features; 3), dosing matrices tailored for fast vs. slow insulin responders; and 4), improved controller safety when encountering CGM sensor anomalies in the clinical environment. Where possible, clinical data from our JDRF-funded trial will be used to further validate the new controller features. The feasibility of the proposed improvements has been demonstrated by initial research. Part 2 of this research, occurring during the second year of this grant, focuses on the clinical evaluation of those features under various parameters, to establish the next major configuration of the FL controller. The technical question for Part 2 is whether the alterations in the controller in silico can be translated into actual improvement in humans. The primary goal is the avoidance of hypoglycemia. Successful completion of these studies could lead to the development of a fully closed loop, commercially available artificial pancreas. The resulting FL controller software product will be offered to commercial diabetes medical device manufactures as a choice for an AP control algorithm. PUBLIC HEALTH RELEVANCE: Successful completion of this study could lead to the development of a safe and effective, fully closed- loop, commercially available, artificial pancreas (AP). The resulting FL controller software product will be offered to commercial diabetes medical device manufactures as a choice for an AP control algorithm.

描述（由申请人提供）：本研究的最终技术目标是开发一种人工胰腺（AP）控制器，通过强有力地预防低血糖发作来改善代谢控制并减少血糖波动。比例积分微分控制（PID）和模型预测控制（MPC）被广泛认为是葡萄糖控制的有希望的候选者。然而，PID和MPC方法依赖于模型，并且由于许多生理限制，例如未测量的膳食量和频繁的胰岛素敏感性变化，建立一个好的T1DM模型并不容易。无模型方法，例如模糊逻辑（FL）控制，为改善血糖控制提供了不同且有前途的方向。 FL 的另一个好处是与传统控制器相比，它所需的计算量较低，从而可以使用更小的 CPU、更低的功耗和更小的电池，所有这些都可以带来更可用的 AP 系统。这项研究旨在提高我们 2010 年 11 月 JDRF 资助的临床试验中使用的控制器的血糖控制和低血糖预防能力。具体来说，本研究的第 1 部分将使用 UVA 模拟器来评估我们现有 FL 控制器的四个具体潜在改进：1) 采用反应性和预测算法的低血糖悬浮 (LGS) 功能； 2）新颖的控制器个性化和在线适配功能； 3)、针对快速和慢速胰岛素反应者量身定制的剂量矩阵； 4)、提高了在临床环境中遇到CGM传感器异常时控制器的安全性。在可能的情况下，我们 JDRF 资助的试验的临床数据将用于进一步验证新的控制器功能。初步研究证明了所提出的改进的可行性。这项研究的第二部分发生在本次拨款的第二年，重点是在各种参数下对这些功能进行临床评估，以确定 FL 控制器的下一个主要配置。第 2 部分的技术问题是计算机中控制器的改变是否可以转化为人类的实际改进。主要目标是避免低血糖。这些研究的成功完成可能会导致开发出完全闭环的、商业化的人工胰腺。由此产生的 FL 控制器软件产品将提供给商业糖尿病医疗设备制造商，作为 AP 控制算法的选择。 公众健康相关性：成功完成这项研究可能会导致安全有效、全闭环、商业化的人工胰腺（AP）的开发。由此产生的 FL 控制器软件产品将提供给商业糖尿病医疗设备制造商，作为 AP 控制算法的选择。