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

描述(由申请人提供)：该项目的目标是通过优化闭合循环系统来改进1型糖尿病的治疗：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型糖尿病患者的低血糖，这是因为工作肌肉对葡萄糖的摄取迅速增加，以及胰岛素敏感性的增加。到目前为止，使用当前的闭环系统来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)算法被设计为对感觉到的血糖下降做出反应，而且这种下降可能会从运动开始时显著延迟。在这个项目中，我们建议将加速测量和心率监测结合起来，以便进行运动检测和分级，以便能够更快地进行算法调整，从而显著降低运动后低血糖的风险。首先，15名患有1型的成人受试者 糖尿病患者将在佩戴加速计和心率监测器的情况下接受两项闭环研究。受试者将在跑步机和自行车上以不同的强度进行锻炼，这些数据将被用来开发一种检测和评分练习的方法。血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升糖素，但不需要修改以检测运动。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的变化，敏感性估计的更新间隔，并将提高血糖目标，这将有效地呼吁更早地给予胰高血糖素。在第二项研究中，受试者将被带到三个闭环研究中：使用未经修改的算法的对照研究，使用APE算法的第二个研究，以及使用优化的APE算法的第三个研究。我们假设AP算法的使用将显著降低低血糖的频率和严重程度。在上述研究的同时，Jacob博士和Castle博士的团队将与Tandem糖尿病和太平洋糖尿病技术公司的工程师合作，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。最终的微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。这种导管将作为胰岛素和胰升糖素输送的管道，使用双腔泵进行输注。SC还具有多个血糖传感单元，这些血糖值将通过蓝牙Low Energy传输到运行APE算法的智能手机。这一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.