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

项目摘要 1型糖尿病(T1D)是一种以胰岛β破坏和随后的胰岛素耗竭为特征的疾病。这个 血糖动力学的改变是令人难以置信的难以管理的，而且会受到饮食、运动、月经、 和压力。尽管胰岛素自动给药系统正在商业化，但绝大多数 患有T1D的患者每天要多次注射胰岛素(MDI)。低血糖的危险并发症 而糖尿病酮症酸中毒可能是由于没有正确服用胰岛素引起的，但要警惕地坚持使用乏味的胰岛素。 给药策略对于MDI用户来说是困难的。这一难度在锻炼过程中被放大，这对 改善糖尿病的长期并发症；即使遵循胰岛素剂量调整指南， 运动时会出现急性低血糖，夜间也会出现低血糖。在我们最近对1400名生活在一起的人的调查中 对于T1D，大多数接受MDI治疗的受试者在运动过程中对控制血糖没有信心，并感到 他们缺乏这样做的工具。总体而言，困难的治疗计划和团注计算，相关的急性 日常活动的并发症，以及这种慢性病的情感和心理损失可能会导致治疗 不坚持和不良的血糖结果。因此，迫切需要决策支持工具来设计用于 计量吸入器使用者改善对饮食、日常活动和锻炼的血糖控制。这项提议的目标是 为使用连续血糖监测系统的1型糖尿病患者开发决策支持工具 每日多次注射疗法。这个工具将被称为miTREAT，即多次注射治疗推荐系统 用于锻炼意识的治疗。我们假设使用一种配备了基于内容的新型决策支持工具 协同过滤方法和动态运动低血糖预测算法将全面提高 对于接受MDI治疗的患者来说，它可以降低血糖，减少低血糖所花费的时间。在我们的第一个目标中，我们将利用 几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，以开发 新的决策支持系统，识别血糖控制中的问题，建议适当的胰岛素剂量和 行为调整。在我们的第二个目标中，我们将开发一个新的练习模型，它既反映了 通过GLUT-4通道快速摄取葡萄糖和纵向双相胰岛素敏感性图谱。这是一项新的 模型结构将被用来预测运动期间和运动后的低血糖。在第三个目标中，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 我们在OHSU开发的一款新的智能手机应用程序的可用性，该应用程序旨在帮助MDI用户。在实现这些目标方面 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强化MDI治疗的患者。该系统将改善正常血糖的总体时间，并减少 运动周围急性并发症的发生。

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.