Multivariable Closed Loop Technologies for Physically Active Young Adults with Ty

描述(申请人提供)：1型糖尿病患者希望享受无忧无虑和积极的生活方式，进行体育活动和锻炼计划。不需要手动输入患者的饮食或体力活动信息的闭环式胰岛素泵可以满足这些愿望。但传感器信息的解释和控制系统对重大新陈代谢变化的适应是至关重要的。这就需要数学模型来准确地表示患者的代谢状态，因为她/他的代谢状态因一系列原因而变化，如饮食、体力活动或压力。对于建立小型化设备的闭环控制系统，详细的非线性模型并不具有吸引力。它们很难为每个受试者及其随时间的新陈代谢变化进行调整，而且它们消耗了大量的计算资源。另一种选择是简单的递归模型，这些模型在每次采样时更新，以适应对象的当前状态。该项目专注于以下方面的开发和临床评估：(1)使用皮下血糖测量和生理数据测量体力活动和应激的递归患者动态模型，以提供准确的血糖浓度预测；(2)低血糖早期预警系统；以及(3)基于这些递归模型的自适应控制器，以控制胰岛素输注速度。18-25岁年龄段的年轻人将是这项研究的重点。连续血糖监测仪(CGM)将提供葡萄糖浓度信息。来自臂章身体监测系统的生理信号将提供代谢/生理信息。取消患者手动输入将减少他们在日常生活中遇到的不便和可能出现的人为错误。将建立多输入(测量血糖浓度和代谢/生理信息)单输出(预测血糖浓度)模型，用于低血糖预警和闭环控制系统。广义预测控制器(GPC)和自校正调节器将被开发用于通过控制胰岛素输注速率来调节血糖水平。建模和控制技术的性能将通过使用详细隔室模型的模拟研究来评估，如在矽肺患者中进行的模拟研究，以及在芝加哥生物医学的普通临床研究中心进行的临床研究。该项目是伊利诺伊理工学院、芝加哥大学医学中心(现已更名为芝加哥生物医学)、伊利诺伊大学芝加哥分校和爱荷华州立大学共同努力的成果。工程学、医学和护理学的协同努力与拟议研究的“从床到床”设计相结合，符合转化性研究的目标。 与公共健康相关：基于递归模型和自适应控制器的闭环血糖浓度控制系统不需要患者的任何手动输入，对生活方式活跃的1型糖尿病年轻人非常有吸引力。这项研究将提供特定于患者的动态模型，通过使用皮下血糖测量和测量体力活动和压力的生理数据来准确预测血糖浓度，提供低血糖早期预警系统，以及基于这些模型通过操纵胰岛素输注速度来调节血糖浓度的自适应控制器。

DESCRIPTION (provided by applicant): Patients with type 1 diabetes would like to enjoy carefree and active lifestyles, conduct physical activities and exercise programs. A closed-loop insulin pump that does not necessitate manual inputs such as meal or physical activity information from the patient can accommodate these wishes. But the interpretation of sensor information and adaptation of the control system to significant metabolic variations is critical. This necessitates mathematical models that can represent the patient's state accurately as her/his metabolic state changes due to a wide spectrum of causes such as meals, physical activity, or stress. Detailed nonlinear models are not attractive for building the closed-loop control systems for miniaturized devices. They are difficult to adjust for each subject and for their metabolic variations over time and they consume significant computational resources. The alternative is simple recursive models that are updated at each sampling time to adapt to the current state of the subject. This project focuses on the development and clinical evaluation of: (1) Recursive patient-specific dynamic models using subcutaneous glucose measurements and physiological data that measure physical activity and stress to provide accurate predictions of blood glucose concentrations; (2) Early warning systems for hypoglycemia; and (3) Adaptive controllers based on these recursive models to manipulate the insulin infusion rate. Young adults in the 18-25 age group will be the focus of the study. Continuous glucose monitors (CGM) will provide glucose concentration information. Physiological signals from an armband body monitoring system will provide the metabolic/physiological information. The elimination of manual inputs entered by patients will reduce the inconveniences that they are experiencing on a daily basis and potential for human errors. Multiple-input (measured glucose concentration and metabolic/physiological information) single-output (predicted glucose concentration) models will be developed for the hypoglycemia warning and closed-loop control system. Generalized predictive controllers (GPC) and self-tuning regulators will be developed for regulating the blood glucose level by manipulating the insulin infusion rate. The performance of the modeling and control techniques will be evaluated by simulation studies using detailed compartmental models as in silico patients and clinical studies conducted at the General Clinical Research Center at Chicago Biomedicine. This project is a collaborative effort between Illinois Institute of Technology, University of Chicago Medical Center (now renamed Chicago Biomedicine), University of Illinois Chicago, and Iowa State University. The collaborative efforts of engineering, medicine and nursing combined with the "bench to bedside" design of the proposed study is consistent with the goals of translational research. PUBLIC HEALTH RELEVANCE: Closed-loop glucose concentration control systems based on recursive models and adaptive controllers that do not necessitate any manual inputs from the patient would be very appealing to young adults with type 1 diabetes who have active lifestyles. This research will provide patient-specific dynamic models that predict blood glucose concentrations accurately by using subcutaneous glucose measurements and physiological data that measure physical activity and stress, early warning systems for hypoglycemia, and adaptive controllers based on these models that regulate blood glucose concentration by manipulating the insulin infusion rate.