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

描述（由申请人提供）：本项目的目的是通过优化闭环系统来改善1型糖尿病的治疗，具体方法是：1）结合运动检测以降低低血糖风险; 2）通过减少系统组件数量来改善可用性。运动通常会导致1型糖尿病患者的低血糖，这是由于工作肌肉对葡萄糖的摄取迅速增加以及胰岛素敏感性增加。迄今为止，使用当前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）算法旨在对感知到的葡萄糖下降做出反应，并且这种下降可能会从运动开始时显着延迟。在本项目中，我们建议结合加速度计和心率监测，以允许运动检测和分级，从而实现更即时的算法调整，以显著降低运动后低血糖的风险。首先，15名患有1型糖尿病的成年受试者 糖尿病患者将在佩戴加速度计和心率监测器的同时进行两次闭环研究。受试者将在不同强度水平的跑步机和自行车上锻炼，这些数据将用于开发检测和分级锻炼的方法。葡萄糖控制将由我们团队目前的双激素AP算法管理，该算法自动提供胰岛素和胰高血糖素，但没有修改以检测运动。我们目前的AP系统的性能，以及从模拟的结果将指导设计的自适应个性化的感知（APE）算法。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，受试者将参与三项闭环研究：使用未修改算法的对照研究、使用APE算法的第二项研究和使用优化APE算法的第三项研究。我们假设使用AP算法将显著降低低血糖的频率和严重程度。与上述研究同时，Jacob和Castle博士的团队将与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的两用葡萄糖传感导管（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，将使用双腔泵输注。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。这种小型化系统将在住院和门诊闭环研究中进行测试。

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.