Advanced Hypoglycemia Prevention Capabilities in Fuzzy Logic Artificial Pancreas

描述(申请人提供)：这项研究的最终技术目标是开发一种人工胰腺(AP)控制器，通过强有力地防止低血糖发作来改善代谢控制并减少血糖漂移。比例-积分-微分控制(PID)和模型预测控制(MPC)被广泛认为是血糖控制的最有前途的候选方案。然而，传统的PID和MPC方法依赖于模型，而且由于一些生理限制，例如无法测量的膳食大小和频繁的胰岛素敏感性变化，很难建立一个好的T1 DM模型。无模型方法，如模糊逻辑(FL)控制，为改进的血糖控制提供了一个不同的和有前途的方向。FL的另一个好处是，与传统控制器相比，它需要的计算需求较低，这导致更小的CPU、更低的功率要求和更小的电池，所有这些都导致更可用的AP系统。这项研究旨在改善我们2010-11年度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。具体地说，本研究的第1部分将使用UVA模拟器来评估我们目前FL控制器的四个具体潜在改进：1)采用反应和预测算法的低糖悬浮(LGS)功能；2)新颖的控制器个性化和在线适应功能；3)针对快速和慢速胰岛素响应者量身定做的剂量矩阵；以及4)提高了在临床环境中遇到CGM传感器异常时的控制器安全性。在可能的情况下，我们将使用JDRF资助的试验的临床数据来进一步验证新的控制器功能。初步研究表明，提出的改进措施是可行的。这项研究的第二部分，发生在这项拨款的第二年，重点是在不同参数下对这些特征的临床评估，以建立FL控制器的下一个主要配置。第二部分的技术问题是，硅胶控制器的改变能否转化为对人类的实际改善。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一种完全闭合的、商业上可用的人工胰腺。由此产生的FL控制器软件产品将作为AP控制算法的选择提供给商业糖尿病医疗设备制造商。 公共卫生相关性：这项研究的成功完成可能导致开发一种安全有效、完全闭合的、商业上可用的人工胰腺(AP)。由此产生的FL控制器软件产品将作为AP控制算法的选择提供给商业糖尿病医疗设备制造商。

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.