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Mitigating risk in a closed loop system by exercise detection and miniaturization

通过运动检测和小型化降低闭环系统中的风险

基本信息

批准号：
8639368
负责人：
Jessica R Castle
金额：
$ 294.33万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-30 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8639368
关键词：
Address Adult Algorithms Amputation Artificial Pancreas Bicycling Blindness Blood Glucose Car Phone Catheters Clinical Clinical Research Communication Controlled Study Data Detection Devices Diabetes Mellitus Diagnosis Emotions Engineering Event Exercise Eye diseases Frequencies Generations Glucagon Glucose Heart Rate Home environment Hormonal Hormones Hour Hypoglycemia Individual Infusion procedures Inpatients Insulin Insulin-Dependent Diabetes Mellitus Kidney Failure Letters Life Machine Learning Measures Metabolism Methods Miniaturization Modeling Modification Monitor Muscle Myocardial Infarction Outpatients Patients Performance Persons Power Sources Pump Quality of life Risk Risk Factors Running Severities Simulate Societies Study Subject System Techniques Technology Telephone Testing Training Update Work base blood glucose regulation cost design glucose sensor glucose uptake improved insulin sensitivity miniaturize public health relevance response sensor simulation strength training success usability

项目摘要

DESCRIPTION (provided by applicant): The objective of this project is to improve treatments for type 1 diabetes via optimization of a closed loop system by 1) incorporating exercise detection to reduce the risk of hypoglycemia and 2) improving usability by reducing the number of system components. Exercise commonly results in hypoglycemia in persons with type 1 diabetes due to a rapid increase in glucose uptake by working muscles as well as an increase in insulin sensitivity. To date, avoiding exercise-induced hypoglycemia using current closed loop systems has had limited success as most artificial pancreas (AP) algorithms are designed to react to declines in sensed glucose, and this decline may be significantly delayed from the onset of exercise. In this project, we propose to incorporate accelerometry and heart rate monitoring to allow for exercise detection and grading to enable more immediate algorithm adjustments to significantly decrease the risk of post-exercise hypoglycemia. First, 15 adult subjects with type 1 diabetes will be brought in for two closed loop studies while wearing an accelerometer and heart rate monitor. Subjects will exercise on a treadmill and bicycle at varying levels of intensity and these data will be utilized to develop a method of detecting and grading exercise. Glucose control will be managed by our team's current bi-hormonal AP algorithm, which automatically delivers insulin and glucagon, but without modifications to detect exercise. The performance of our current AP system as well as results from simulations will guide the design of an Adaptive Personalized Exercise-sensing (APE) algorithm. We anticipate the APE algorithm will effect changes in estimated insulin sensitivity, the update interval of the sensitivity estimates, and wil raise the glucose target which will effectively call for glucagon delivery earlier. In a second study, subjects will be brought in for three closed loop studies: a control study using the unmodified algorithm, a second study utilizing the APE algorithm, and a third study with an optimized APE algorithm. We hypothesize that use of the AP algorithm will significantly reduce the frequency and severity of hypoglycemia. In parallel with the above studies, the team of Drs. Jacob and Castle will be collaborating with the engineers of Tandem Diabetes and Pacific Diabetes Technologies to reduce the number of devices which the patient must wear or carry from 12 eventually down to 3. The final miniaturized system will include integration of a new dual- purpose glucose-sensing catheter (SC) into the AP platform. This catheter will serve as a conduit for insulin and glucagon delivery, which will be infused using a dual chamber pump. The SC also has multiple glucose sensing units, and these glucose values will be transmitted via Bluetooth Low Energy to a smart phone that will run the APE algorithm. This miniaturized system will be tested in inpatient and outpatient closed loop studies.

描述（由申请人提供）：该项目的目标是通过优化闭环系统来改善 1 型糖尿病的治疗，方法是：1）结合运动检测以降低低血糖风险；2）通过减少系统组件的数量来提高可用性。运动通常会导致 1 型糖尿病患者发生低血糖，因为运动肌肉的葡萄糖摄取迅速增加以及胰岛素敏感性增加。迄今为止，使用当前的闭环系统避免运动引起的低血糖的成功有限，因为大多数人工胰腺（AP）算法旨在对感测到的血糖下降做出反应，并且这种下降可能会从运动开始后显着延迟。在这个项目中，我们建议结合加速度测量和心率监测来进行运动检测和分级，从而实现更即时的算法调整，从而显着降低运动后低血糖的风险。首先，15 名 1 型成人受试者糖尿病患者将被纳入两项闭环研究，同时佩戴加速计和心率监测器。受试者将在跑步机和自行车上进行不同强度的锻炼，这些数据将用于开发检测和分级锻炼的方法。血糖控制将由我们团队当前的双激素 AP 算法进行管理，该算法会自动提供胰岛素和胰高血糖素，但无需修改即可检测运动。我们当前 AP 系统的性能以及模拟结果将指导自适应个性化运动感知 (APE) 算法的设计。我们预计 APE 算法将影响估计的胰岛素敏感性、敏感性估计的更新间隔的变化，并将提高葡萄糖目标，这将有效地要求更早地输送胰高血糖素。在第二项研究中，受试者将被纳入三项闭环研究：一项使用未经修改的算法的对照研究，第二项使用 APE 算法的研究，以及第三项使用优化 APE 算法的研究。我们假设使用 AP 算法将显着降低低血糖的频率和严重程度。与上述研究同时进行的，博士团队。 Jacob 和 Castle 将与 Tandem Diabetes 和 Pacific Diabetes Technologies 的工程师合作，将患者必须佩戴或携带的设备数量从 12 个最终减少到 3 个。最终的小型化系统将包括将新型双用途葡萄糖传感导管 (SC) 集成到 AP 平台中。该导管将用作胰岛素和胰高血糖素输送的导管，将使用双室泵进行输注。 SC 还具有多个葡萄糖传感单元，这些葡萄糖值将通过低功耗蓝牙传输到运行 APE 算法的智能手机。该小型化系统将在住院和门诊闭环研究中进行测试。