On-body ecosystem for automated insulin delivery in type 1 diabetes

项目摘要 尽管有连续血糖监测仪（CGM）和胰岛素泵，但相对较少的人 1型糖尿病（T1D）符合推荐的血糖目标;此外，严重低血糖的发生率仍然 不可接受的高。这些次优结果对于儿童、青少年和年轻人尤其如此 关于T1D自动"闭环"（CL）胰岛素输送系统（所谓的"人工胰腺"[AP]）的承诺 改善血糖控制，减少低血糖，同时保持生活质量。CL的早期世代 系统已经显示出对血糖结果的有益效果，但是它们的设计和形状因素可能不 最佳用户满意度和持续用途：大多数系统使用管式胰岛素泵和智能手机 控制器设备，两个挑战长期可用性的关键系统组件。终极普遍性 这些技术的采用不仅取决于它们的有效性和安全性，还取决于"人类"的健康状况。 与患者和家属感知的易用性和舒适性相关的“因素”。此外，大多数控制 算法不允许定制血糖参数或警告和警报。我们相信一个 完全“在体”系统具有明显的优势，包括：在活动期间的功能，如运动和 淋浴;更少的不同设备带来更高的用户满意度; 向用户和/或家庭成员的智能电话发送可定制的远程警报消息。这些改进 可能会最大限度地提高血糖和生活质量。本申请提出了一个迭代系列的研究 这将使开发一个完全的身体CL系统的基础上验证模型预测 控制（MPC）由Dassau和Doyle开发的AP策略，通过自适应学习模块进行增强 以及基于个人需求的可定制、用户可编程的警报消息。我们的三个具体目标 [目标是有效、安全地控制血糖水平、用户满意度和生活质量]如下： 具体目标1：开发和评估自动胰岛素输注闭环系统， 完全"在体"生态系统，用于儿童、青少年和年轻成人T1D受试者。一个- 身体CL系统将在一系列可行性研究中开发和评估，从年轻人开始， 转移到青少年和更小的孩子。 具体目标2：设计一个可定制的远程警报消息传递系统， CL系统。我们将评估患者和家属在可用性、可接受性和警报/报警方面的偏好 通过焦点小组/半结构化访谈以及对研究对象和家庭成员的调查， 目标是开发一个平台， 具体目标3：开发一个自适应学习模块，用于CL系统。自适应 学习控制算法的设计，以提高性能随着时间的推移将评估在一系列的家庭- 基于有效性研究，从年轻人开始，然后转移到青少年和更年幼的儿童。

PROJECT SUMMARY Despite the availability of continuous glucose monitors (CGM) and insulin pumps, relatively few persons with type 1 diabetes (T1D) meet recommended glycemic targets; furthermore, rates of severe hypoglycemia remain unacceptably high. These suboptimal outcomes are especially true for children, adolescents, and young adults with T1D. Automated “closed-loop” (CL) insulin delivery systems (so-called “artificial pancreas” [AP]) promise to improve glucose control and reduce hypoglycemia while preserving quality of life. Early generations of CL systems have shown beneficial effects on glycemic outcomes, but their design and form factors may not be optimal for user satisfaction and continued use: most systems use a tubed insulin pump and smartphone controller device, two critical system components that challenge long-term usability. Ultimate universal adoption of these technologies will depend not only on their effectiveness and safety, but also on “human factors” related to ease and comfort of use, as perceived by the patient and family. Further, most control algorithms do not allow for customization of glycemic parameters or alerts and alarms. We believe that a completely “on-body” system has distinct advantages, including: function during activities such as exercise and showering; greater user satisfaction with fewer disparate devices; and additional benefits can be realized with customizable, remote alert messaging to smartphones of users and/or family members. These improvements will likely maximize glycemic and quality of life benefits. This application presents an iterative series of studies that will enable the development of a completely on-body CL system based on the validated Model Predictive Control (MPC) AP strategy developed by Dassau and Doyle that is enhanced with an adaptive learning module and customizable, user-programmable alert messages based on individual needs. Our 3 specific aims [targeting effective and safe control of BG levels, user satisfaction, and quality of life] follow: Specific Aim 1: Develop and evaluate an automated insulin delivery closed-loop system with a fully “on-body” ecosystem for use in pediatric, adolescent, and young adult subjects with T1D. An on- body CL system will be developed and evaluated in a series of feasibility studies, starting in young adults and moving to adolescents and then younger children. Specific Aim 2: Design a customizable, remote alert messaging system for use with the on-body CL system. We will assess patient and family preferences regarding usability, acceptability, and alert/alarm settings through focus groups/semi-structured interviews and surveys of study subjects and family members, with the goal to develop a platform that enables alert customization Specific Aim 3: Develop an adaptive learning module for use with the CL system. An adaptive learning control algorithm designed to improve performance over time will be evaluated in a series of home- based effectiveness studies, starting in young adults and moving to adolescents and then younger children.