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

基本信息

批准号：
7939934
负责人：
Ali Cinar
金额：
$ 21.8万
依托单位：
ILLINOIS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7939934
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 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 operation pre-clinical predictive modeling premature programs public health relevance research clinical testing sensor simulation statistics subcutaneous treatment strategy young adult

项目摘要

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、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膳食或身体活动信息的闭环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显着代谢变化的适应至关重要。这就需要数学模型能够准确地表示患者的代谢状态由于膳食、体力活动或压力等多种原因而变化时的状态。详细的非线性模型对于构建小型设备的闭环控制系统没有吸引力。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，并且消耗大量的计算资源。另一种选择是简单的递归模型，该模型在每个采样时间进行更新以适应主体的当前状态。该项目重点开发和临床评估：（1）使用皮下葡萄糖测量和测量身体活动和压力的生理数据的递归患者特定动态模型，以提供血糖浓度的准确预测； (2)低血糖预警系统； (3)基于这些递归模型的自适应控制器来操纵胰岛素输注速率。 18-25岁年龄段的年轻人将是研究的重点。连续血糖监测仪 (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。消除患者手动输入将减少他们每天遇到的不便和潜在的人为错误。将为低血糖预警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。将开发广义预测控制器（GPC）和自调节调节器，通过操纵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的隔室模型（如计算机患者）的模拟研究以及在芝加哥生物医学综合临床研究中心进行的临床研究来评估。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中心）、伊利诺伊大学芝加哥分校和爱荷华州立大学之间的合作成果。工程、医学和护理的协作努力与拟议研究的“从实验室到临床”的设计相结合，与转化研究的目标是一致的。公共健康相关性：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进行任何手动输入，这对于生活方式积极的 1 型糖尿病年轻人非常有吸引力。这项研究将提供针对患者的动态模型，通过使用皮下葡萄糖测量值和测量体力活动和压力的生理数据来准确预测血糖浓度，低血糖预警系统，以及基于这些模型的自适应控制器，通过操纵来调节血糖浓度胰岛素输注速度。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Adaptive control of artificial pancreas systems - a review.

人工胰腺系统的自适应控制 - 综述。

DOI：
发表时间：
2014
期刊：
影响因子：
0
作者：
Turksoy, Kamuran;Cinar, Ali
通讯作者：
Cinar, Ali

Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement.

人工胰腺的多变量自适应闭环控制，无需进餐和活动通知。

DOI：
发表时间：
2013-05
期刊：
影响因子：
5.4
作者：
Turksoy, Kamuran;Bayrak, Elif Seyma;Quinn, Lauretta;Littlejohn, Elizabeth;Cinar, Ali
通讯作者：
Cinar, Ali

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

基于递归自回归偏最小二乘模型的低血糖早期报警系统。

DOI：
发表时间：
2013-01-01
期刊：
影响因子：
0
作者：
Bayrak, Elif Seyma;Turksoy, Kamuran;Cinar, Ali;Quinn, Lauretta;Littlejohn, Elizabeth;Rollins, Derrick
通讯作者：
Rollins, Derrick

Hypoglycemia Early Alarm Systems Based On Multivariable Models.

基于多变量模型的低血糖早期报警系统。