Statistical Problems in Closed-Loop Diabetes Control

闭环糖尿病控制中的统计问题

• 批准号: 1106753
• 负责人: Cun-Hui Zhang
• 金额: $ 30万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1106753&HistoricalAwards=false
• 关键词: Statistical; Problems; Closed; Loop; Diabetes

The project focuses on developing statistical models, methods and related theory for closed-loop diabetes control. An artificial pancreas with a closed-loop insulin delivery system, still in an early stage of its development, is expected to revolutionize the way diabetes is treated. As the PIs and many others recognized, a major impediment to the goal of developing an artificial pancreas is the unreliability of the glucose sensor. Sensor technology is not so new and it is also remarkably clever, but frequent recalibration is needed because of the physical processes underlying the way sensors work. The proposed project will solve the problem of proper recalibration by appropriate physical modeling of the metabolic processes involved. Sensors measure current in interstitial space (subcutaneous fat) at fixed time increments all around the clock. The current is nominally proportional to the amount of glucose in the space, but there are two problems with the measurements. Firstly, there is a delay in diffusion of glucose from the bloodstream into fat so that the glucose density in the fat lags the glucose density in the bloodstream. Secondly, there is a defense mechanism (white blood cells) which surround the electrode as a foreign body and attempt to get rid of it via biofouling. The white blood cells interfere with current flow and produce erroneous measurements unless accurate recalibration is performed. The proposed approach has not been tried and is expected to significantly outperform the current implementation of the sensor technology based on more straightforward regression without gaining the benefit and insight of the proposed physical modeling. A new differential equation approach will be used to deal with the delay problem related to glucose sensor. The differential equation is widely accepted and the rate for the diffusion of glucose from the blood into interstitial space governs the relationship between the glucose densities in the bloodstream and interstitial spaces. The diffusion rate and the effect of biofouling will be estimated from finger stick metered measurements taken a few times a day. A main innovation in the proposed approach is statistical models using the physics of the delay and biofouling problems.An artificial pancreas will be a godsend to the millions of Americans faced with the 24 hour a day tedium of having to decide when and how much insulin to inject. If this could be done automatically for them then the only task they would continue to have is to remember to replace insulin in the well of their insulin pump. An artificial pancreas would give them an effective treatment for their disease.

该项目专注于开发用于闭环糖尿病控制的统计模型、方法和相关理论。一种具有闭环胰岛素输送系统的人工胰腺仍处于开发的早期阶段，预计将彻底改变糖尿病的治疗方式。正如PIS和其他许多人所认识到的那样，开发人工胰腺的目标的一个主要障碍是葡萄糖传感器的不可靠。传感器技术并不是那么新，它也非常聪明，但由于传感器工作方式背后的物理过程，需要频繁地重新校准。拟议的项目将通过对所涉及的代谢过程进行适当的物理模拟来解决适当重新校准的问题。传感器全天候以固定的时间增量测量间质(皮下脂肪)中的电流。电流名义上与空间中的葡萄糖含量成正比，但测量有两个问题。首先，葡萄糖从血液扩散到脂肪的过程有延迟，因此脂肪中的葡萄糖密度落后于血液中的葡萄糖密度。其次，存在一种防御机制(白血球)，它将电极作为异物包围，并试图通过生物污垢将其清除。除非进行准确的重新校准，否则白细胞会干扰电流并产生错误的测量结果。建议的方法尚未尝试，预计将显著优于当前基于更直接回归的传感器技术实现，而不会获得建议的物理模型的好处和洞察力。采用一种新的微分方程法来处理与葡萄糖传感器相关的延迟问题。微分方程式被广泛接受，葡萄糖从血液扩散到间质的速率决定了血液和间质中的葡萄糖密度之间的关系。扩散速度和生物污垢的影响将通过每天进行几次手指棒计量测量来估计。拟议方法的一个主要创新是使用延迟和生物污垢问题的物理学建立统计模型。对于每天24小时不得不决定何时注射多少胰岛素的数百万美国人来说，人工胰腺将是天赐之物。如果这可以为他们自动完成，那么他们继续面临的唯一任务就是记住更换胰岛素泵中的胰岛素。人工胰腺将为他们的疾病提供有效的治疗方法。