ADAPTIVE MOTIF-BASED CONTROL (AMBC): A FUNDAMENTALLY NEW APPROACH TO AUTOMATED TREATMENT OPTIMIZATION FOR TYPE 1 DIABETES

自适应基序控制 (AMBC)：1 型糖尿病自动优化治疗的全新方法

• 批准号: 10684819
• 负责人: SUE A BROWN
• 金额: $ 68.56万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-17 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10684819
• 关键词: Address; Adult; Algorithms; Area Under Curve; Artificial Intelligence; Artificial Pancreas; Cellular Phone; Child; Classification; Clinical; Clinical Trials; Code; Color; Continuous Glucose Monitor; Cross-Over Trials; Crossover Design; Data; Data Science; Databases; Development; Diabetes Mellitus; Enrollment; Europe; European; Exercise; Glucose; Goals; Health; Health Personnel; Hour; Hybrids; Hyperglycemia; Hypoglycemia; Incidence; Individual; Inferior; Injections; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Learning; Libraries; Measures; Medicine; Metabolic; Methods; Modality; Monitor; New England; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Participant; Patient Education; Pattern; Pattern Recognition; Persons; Physical activity; Pilot Projects; Population; Population Database; Process; Proliferating; Property; Protocols documentation; Publishing; Randomized; Recording of previous events; Running; Structure; System; Technology; Testing; Text; Time; advanced system; clinical practice; clinical translation; control trial; data library; data space; deep neural network; design; efficacy evaluation; experience; glycemic control; improved; innovation; new technology; next generation; novel; novel strategies; process optimization; subcutaneous; tool; treatment optimization; trial comparing; trial enrollment; virtual

PROJECT SUMMARY Adaptive Motif-Based Control (AMBC): A Fundamentally New Approach to Automated Treatment Optimization for Type 1 Diabetes Automated Insulin Delivery (AID) has transitioned to the clinical practice and one of the most advanced systems to date –Control-IQ (Tandem, Inc.)– is based on the UVA-AID, developed and tested by our team. Following two pivotal trial in adults and children with type 1 diabetes (T1D), both published in the New England J of Medicine, the system was cleared by the FDA and European regulatory agencies, and is now in use worldwide. With this clinical translation now accomplished, our academic objective is to design and test next-generation AID solutions. The key innovative concept behind the new Adaptive Motif-Based Control (AMBC) class of AID algorithms proposed here, is the ability to learn from a user’s past glycemic-control patterns and from population patterns, and optimize this user’s treatment in real time; thus, unlike any other AID system, the AMBC will utilize both a person’s own history and the history of others, to forecast glycemic changes and adapt AID action accordingly. To achieve its goals, the AMBC will employ: (i) a newly discovered fundamental structure underlying the multitude of possible daily continuous glucose monitoring (CGM) profiles in diabetes, which allows classification of these profiles into a finite number of basic “motifs”, and (ii) a new Adaptation-to-Profile treatment optimization process. To test the AMBC, we propose a pilot study, followed by a randomized cross-over trial enrolling 90 participants with T1D and Control-IQ experience, to compare the UVA-AID (as built in Control-IQ) to 3 treatment modalities: AMBC with meal and exercise announcements; AMBC-A without meal or exercise announcements, i.e. a “full closed-loop,” and an intermediate AMBC-EA which will have meal but no exercise announcement. Participants will be randomized to two groups following different sequences of treatment modalities: UVA-AID-->AMBC- A-->AMBC-EA-->AMBC and AMBC-->AMBC-EA-->AMBC-A-->UVA-AID. Each treatment modality will continue for 5 weeks. This time-tested design enables four crossover comparisons, which will test the following hypotheses: (1) AMBC with meal/exercise announcements will be superior to UVA-AID in terms of time in the range 70- 180mg/dl and reduced incidence of hypoglycemia (measured by CGM), and technology acceptance; (2) AMBC-A without meal/exercise announcements will be non-inferior to UVA-AID in terms of time >180mg/dL during the day, incidence of hypoglycemia during and after exercise, and postprandial glucose variability; (3) Deescalating AMBC-->AMBC-EA-->AMBC-A vs escalating AMBC-A-->AMBC-EA-->AMBC deployment of meal and exercise announcements will have no influence on the outcome within each treatment modality. Overall, we affirm that reliable technology has been developed and sufficient data accumulated to warrant the development of a new class of AID algorithms – AMBC – which is expected to become the first AID system learning from a user’s own historical metabolic patterns and from the patterns of others structured in a new relational data library. We view the AMBC as a paradigm for transition from hybrid to full (no announcements) AID, but do not claim that the AMBC is a full closed-loop system.

项目概要 基于自适应基序的控制（AMBC）：一种全新的自动化治疗方法 1 型糖尿病的优化 自动胰岛素输送 (AID) 已进入临床实践，是最先进的系统之一 迄今为止，Control-IQ（Tandem, Inc.）是基于 UVA-AID，由我们的以下两个团队开发和测试的。 针对成人和儿童 1 型糖尿病 (T1D) 的关键试验，均发表在《新英格兰医学杂志》上， 该系统已获得 FDA 和欧洲监管机构的批准，目前已在全球范围内使用。 临床转化现已完成，我们的学术目标是设计和测试下一代 AID 解决方案。 新型自适应基序控制 (AMBC) 类 AID 算法背后的关键创新概念 这里提出的是从用户过去的血糖控制模式和人群模式中学习的能力， 并实时优化该用户的治疗；因此，与任何其他 AID 系统不同，AMBC 将同时利用 一个人自己的病史和他人的病史，以预测血糖变化并相应地调整援助行动。 为了实现其目标，AMBC 将采用：(i) 新发现的大众基础结构 糖尿病中可能的每日连续血糖监测 (CGM) 概况，从而可以对这些概况进行分类 将轮廓分解为有限数量的基本“基序”，以及（ii）新的适应轮廓治疗优化过程。 为了测试 AMBC，我们提出了一项试点研究，随后进行了一项招募 90 名参与者的随机交叉试验 凭借 T1D 和 Control-IQ 经验，将 UVA-AID（Control-IQ 内置）与 3 种治疗方式进行比较： AMBC 有进餐和锻炼通知；AMBC-A 没有进餐或锻炼通知，即“完整” 闭环”，以及中间 AMBC-EA，参与者将吃饭但没有行权公告。 将被随机分为两组，遵循不同的治疗方式顺序：UVA-AID-->AMBC- A-->AMBC-EA-->AMBC 和 AMBC-->AMBC-EA-->AMBC-A-->UVA-AID 每种治疗方式将继续。 这个经过时间考验的设计可以进行四次交叉比较，这将测试以下假设： (1) 带有膳食/运动通知的 AMBC 在时间方面优于 UVA-AID，范围为 70- 180mg/dl，降低低血糖发生率（通过 CGM 测量），以及技术接受度； (2) AMBC-A（不含进餐/运动公告）在时间方面不劣于 UVA-AID >180mg/dL 白天、运动期间和运动后低血糖的发生率以及餐后血糖变异性； (3) 降级 AMBC-->AMBC-EA-->AMBC-A 与升级 AMBC-A-->AMBC-EA-->AMBC 部署 膳食和运动公告不会对每种治疗方式的结果产生影响。 总体而言，我们确认已经开发出可靠的技术并积累了足够的数据来保证 开发一类新型 AID 算法 – AMBC – 预计将成为第一个 AID 系统 从用户自己的历史代谢模式以及以新的方式构建的其他人的模式中学习 我们将 AMBC 视为从混合型数据库过渡到完整型数据库的范例（未发布公告） AID，但不声称AMBC是一个全闭环系统。

项目成果

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility.

开发 UVA/Padova 1 型糖尿病模拟器：建模、验证、改进和实用。

• 发表时间: 2023-11
• 作者: Cobelli, Claudio;Kovatchev, Boris
• 通讯作者: Kovatchev, Boris

Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm.

神经网络人工胰腺：一流的自动胰岛素输送算法的随机交叉试验。

• 发表时间: 2024-01-26
• 影响因子: 5.4
• 作者: Kovatchev, Boris P;Frasquet, Alberto Castillo;Pryor, Elliott Carroll;Kollar, Laura;Barnett, Charlotte;DeBoer, Mark D;Brown, Sue A
• 通讯作者: Brown, Sue A