Multivariable Closed Loop Technologies for Physically Active Young Adults with Ty

适用于身体活跃的青少年的多变量闭环技术

• 批准号: 7791951
• 负责人: Ali Cinar
• 金额: $ 21.99万
• 依托单位: ILLINOIS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7791951
• 关键词: Address; Adolescent; Amputation; Area; Artificial Pancreas; Automation; Blood Glucose; Blood Vessels; Cardiovascular system; Cessation of life; Chicago; Chronic Disease; Clinical Management; Clinical Research; Collaborations; Complications of Diabetes Mellitus; Computer Simulation; Data; Development; Devices; Diabetes Mellitus; Diagnosis; Discipline of Nursing; Engineering; Ensure; Exercise; Feasibility Studies; Future; Glucose; Goals; Grant; Human; Hyperglycemia; Hypoglycemia; Illinois; Infusion procedures; Injection of therapeutic agent; Institutes; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Iowa; Kidney Diseases; Life; Life Style; Literature; Manuals; Manufacturer Name; Measurement; Measures; Mechanics; Medical center; Medicine; Metabolic; Modeling; Monitor; Neuropathy; Non-Insulin-Dependent Diabetes Mellitus; Non-linear Models; Operative Surgical Procedures; Outcome; Output; Patients; Performance; Peripheral Vascular Diseases; Physical activity; Physiological; Pump; Recruitment Activity; Regulation; Research; Research Proposals; Research Training; Retinal Diseases; Risk; Sampling; Seeds; Series; Signal Transduction; Stress; Study Subject; System; Techniques; Technology; Testing; Time; Time Series Analysis; Translational Research; Universities; Update; Variant; Work; age group; base; bench to bedside; blood glucose regulation; college; computing resources; design; expectation; experience; glucose monitor; glucose sensor; mathematical model; member; miniaturize; model development; new technology; pre-clinical; predictive modeling; premature; programs; public health relevance; research clinical testing; sensor; simulation; statistics; subcutaneous; treatment strategy; young adult

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.

描述（由申请人提供）：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。不需要来自患者的诸如膳食或身体活动信息的手动输入的闭环胰岛素泵可以满足这些愿望。但是，传感器信息的解释和控制系统对显著代谢变化的适应是至关重要的。这就需要数学模型，可以准确地代表病人的状态，因为她/他的代谢状态变化，由于广泛的原因，如膳食，体力活动，或压力。详细的非线性模型是没有吸引力的建立闭环控制系统的小型化设备。它们很难针对每个受试者及其随时间的代谢变化进行调整，并且它们消耗大量的计算资源。另一种方法是简单的递归模型，在每个采样时间更新以适应受试者的当前状态。该项目的重点是开发和临床评价：（1）递归患者特定的动态模型，使用皮下葡萄糖测量和生理数据测量身体活动和压力，以提供血糖浓度的准确预测;（2）低血糖的早期预警系统;和（3）基于这些递归模型的自适应控制器，以操纵胰岛素输注速率。18-25岁年龄组的年轻人将是研究的重点。动态血糖监测仪（CGM）将提供血糖浓度信息。来自臂带身体监测系统的生理信号将提供代谢/生理信息。消除患者输入的手动输入将减少他们每天遇到的不便和人为错误的可能性。将为低血糖警告和闭环控制系统开发多输入（测量的葡萄糖浓度和代谢/生理信息）单输出（预测的葡萄糖浓度）模型。广义预测控制器（GPC）和自校正调节器将被开发用于通过操纵胰岛素输注速率来调节血糖水平。将通过模拟研究评价建模和控制技术的性能，模拟研究使用详细的房室模型（如计算机模拟患者）和在芝加哥生物医学综合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（现更名为芝加哥生物医学）、伊利诺伊大学芝加哥和爱荷华州州立大学之间的合作成果。工程、医学和护理的协同努力与拟议研究的“工作台到床边”设计相结合，符合转化研究的目标。 公共卫生关系：基于递归模型和自适应控制器的闭环葡萄糖浓度控制系统不需要患者的任何手动输入，这对具有积极生活方式的1型糖尿病年轻人非常有吸引力。这项研究将提供患者特定的动态模型，通过使用皮下葡萄糖测量和测量身体活动和压力的生理数据准确预测血糖浓度，低血糖的早期预警系统，以及基于这些模型的自适应控制器，通过操纵胰岛素输注速率来调节血糖浓度。