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

1 型糖尿病深度治疗决策支持引擎的设计和评估

基本信息

批准号：
9898150
负责人：
Nichole Sahar Tyler
金额：
$ 5.05万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9898150
关键词：
Acute Adherence Adult Aerobic Exercise Assimilations Awareness Behavior Behavior Therapy Behavioral Bolus Infusion Cellular Phone Chronic Disease Clinical Sciences Clinical Trials Closure by clamp Complications of Diabetes Mellitus Cross-Over Trials Cutaneous Dangerousness Data Decision Support Systems Diabetic Ketoacidosis Disease Dose Emotional Evaluation Event Exercise Exercise Physiology Failure Fright Glucose Goals Guidelines Hypoglycemia Infusion procedures Injection of therapeutic agent Injections Insulin Insulin-Dependent Diabetes Mellitus Machine Learning Measurement Mediating Medical Technology Menstruation Methods Modeling Modification Outcome Outcome Measure Outcome Study Output Pancreas Patients Pattern Performance Pilot Projects Plant Roots Recommendation Recording of previous events Recurrence Regimen Research Running SLC2A1 gene Site Stress Structural Models Study Subject Surveys System Telephone Time Tracer Treatment Protocols Trust Variant Weight base blood glucose regulation computer science design diabetes management dosage e-commerce euglycemia exercise intensity glucose disposal glucose monitor glucose uptake glycemic control heuristics human subject improved in silico in vivo insulin sensitivity mathematical model novel prediction algorithm prevent primary outcome psychologic resistance exercise response secondary outcome smartphone Application standard of care support tools tool usability

项目摘要

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.

项目概要 1 型糖尿病 (T1D) 是一种以胰腺 β 破坏和随后的胰岛素耗竭为特征的疾病。这血糖动态的变化非常难以控制，并且会受到膳食、运动、月经、和压力。尽管自动胰岛素输送系统正在商业化，但大多数 1 型糖尿病患者每天接受多次注射胰岛素 (MDI) 治疗。低血糖的危险并发症糖尿病酮症酸中毒可能是由于未能正确服用胰岛素而引起的，但要警惕地坚持使用繁琐的胰岛素 MDI 用户很难维持剂量策略。这种困难在运动过程中会被放大，这对于改善糖尿病的长期并发症；即使遵循胰岛素剂量调整指南，可发生运动时急性低血糖和夜间低血糖。我们最近对 1400 名生活在对于 T1D，大多数接受 MDI 治疗的受试者对运动期间控制血糖没有信心，并且感觉他们缺乏这样做的工具。总的来说，困难的治疗计划和推注计算，与急性日常活动引起的并发症以及这种慢性疾病造成的情感和心理损失可能需要治疗不依从和血糖结果不佳。因此，迫切需要专门设计的决策支持工具 MDI 用户可改善膳食、日常活动和锻炼的血糖控制。该提案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。该工具将被称为 miTREAT，多次注射治疗推荐系统用于运动意识疗法。我们假设使用配备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得到整体改善血糖正常并减少接受 MDI 治疗的患者发生低血糖的时间。在我们的第一个目标中，我们将利用数十年的计算机科学推荐系统和机器学习优化策略研究，开发了新颖的决策支持系统，可识别血糖控制中的问题并推荐适当的胰岛素剂量和行为改变。在我们的第二个目标中，我们将开发一种新的运动模型，反映通过 GLUT-4 通道快速摄取葡萄糖和纵向双相胰岛素敏感性曲线。这个新的模型结构将用于预测运动期间和运动后的低血糖。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。该临床试验将评估我们在 OHSU 开发的新智能手机应用程序旨在帮助 MDI 用户的可用性。在实现这些目标，我们将开发第一个决策支持系统，为患者提供治疗和行为建议接受 CGM 增强 MDI 治疗的患者。该系统将改善血糖正常的总体时间，并减少运动周围急性并发症的发生。