Design and Evaluation of a Decision Support Engine for Advanced Treatment of Type 1 Diabetes

项目摘要 1型糖尿病（T1 D）是一种以胰腺β破坏和随后的胰岛素耗竭为特征的疾病。的 葡萄糖动力学的改变非常难以控制，并且会受到膳食、运动、月经 和压力尽管自动化胰岛素输送系统正变得可商购获得，但是绝大多数 患有T1 D的人通过每天多次注射胰岛素（MDI）来治疗。低血糖的危险并发症 和糖尿病酮症酸中毒可发生在未能正确剂量的胰岛素，但警惕坚持繁琐的胰岛素 计量吸入器使用者难以维持给药策略。这种困难在运动过程中被放大，这对 改善糖尿病的长期并发症;即使当遵循胰岛素剂量调整的指导方针时， 可能发生运动期间的急性低血糖和夜间低血糖。在我们最近对1400名生活在 对于T1 D，大多数接受MDI治疗的受试者对运动期间的血糖管理没有信心， 他们缺乏这样做的工具。总的来说，治疗计划和推注计算困难，相关急性 日常活动的并发症，以及这种慢性疾病的情绪和心理代价可能导致治疗 不依从和血糖结果差。因此，迫切需要为以下目的而设计的决策支持工具： MDI使用者改善饮食、日常活动和锻炼时的血糖控制。该提案的目标是 为使用连续葡萄糖监测系统的1型糖尿病患者开发决策支持工具， 每日多次注射治疗。该工具将被称为miTREAT，即多次注射治疗推荐系统 用于运动意识疗法。我们假设，使用一种新的决策支持工具，配备了基于内容的 协同过滤方法和动态运动低血糖预测算法将整体改善 并减少MDI治疗患者的低血糖时间。在我们的第一个目标中， 数十年来在计算机科学推荐系统和机器学习优化策略方面的研究， 新型决策支持系统，可识别血糖控制问题并推荐适当的胰岛素剂量， 行为改变在我们的第二个目标中，我们将开发一种新的锻炼模式， 通过GLUT-4通道和纵向双相胰岛素敏感性曲线快速摄取葡萄糖。这个新 模型结构将用于预测运动期间和之后的低血糖。在我们的第三个目标中，我们将 探索我们的决策支持引擎在体内临床试验中的性能。这项临床试验将评估 一个新的智能手机应用程序的可用性，旨在帮助MDI用户，我们已经在OHSU开发。实现这些 目标，我们将开发第一个决策支持系统，提供治疗和行为建议， 接受CGM增强MDI治疗的患者。该系统将改善欧盟的整体时间，并减少 发生急性并发症周围的运动。

Project Summary Type 1 diabetes (T1D) is a disease characterized by pancreatic beta destruction with subsequent insulin depletion. The alterations in glucose dynamics are incredibly difficult to manage and are confounded by meals, exercise, menstruation, and stress. Although automated insulin delivery systems are becoming commercially available, the large majority of people with T1D are treated with multiple daily injections of insulin (MDI). Dangerous complications of hypoglycemia and diabetic ketoacidosis can occur from failure to dose insulin correctly, however vigilant adherence to tedious insulin dosing strategies are difficult for MDI users maintain. This difficulty is magnified during exercise, which is critical to ameliorating long-term complications of diabetes; even when guidelines for insulin dosage adjustments are followed, acute hypoglycemia during exercise and night-time hypoglycemia can occur. In our recent survey of 1400 subjects living with T1D, the majority of subjects on MDI therapy were not confident in managing their glucose during exercise and felt they lacked tools to do so. In aggregate, difficult treatment schedules and bolus calculations, associated acute complications from daily activities, and the emotional and psychological toll of this chronic disease can result in treatment non-adherence and poor glycemic outcomes. Therefore, there is a critical need for decision support tools designed for MDI users to improve glycemic control surrounding meals, daily activities and exercise. The goal of this proposal to develop a decision support tool for patients with type 1 diabetes who utilize continuous glucose monitoring systems and multiple daily injection therapy. This tool will be called miTREAT, the multiple injection treatment recommender system for exercise-aware therapies. We hypothesize that use of a novel decision support tool equipped with content-based collaborative filtering methods and dynamic exercise hypoglycemia prediction algorithms will improve overall euglycemia and reduce time spent in hypoglycemia for patients on MDI therapy. In our first aim, we will leverage decades of research in computer science recommender systems and machine learning optimization strategies to develop a novel decision support system that identifies issues in glycemic control and recommends appropriate insulin dose and behavioral modifications. In our second aim, we will develop a new exercise model that reflects both the dynamics of rapid-uptake of glucose through GLUT-4 channels and the longitudinal biphasic insulin sensitivity profile. This new model structure will be used to predict hypoglycemia during and after the exercise period. In our third aim, we will explore the performance of our decision support engine in an in-vivo clinical trial. This clinical trial will assess the usability of a new smart-phone app designed to assist MDI users that we have developed at OHSU. In achieving these goals, we will develop the first decision support system that provides treatment and behavioral recommendations to patients on CGM-augmented MDI therapy. This system will improve overall time in euglycemia, and reduce the occurrence of acute complications surrounding exercise.