Hybrid Closed Loop Insulin Pump use in Poorly Controlled Type 1 Diabetes

混合闭环胰岛素泵在控制不佳的 1 型糖尿病中的应用

• 批准号: 10429863
• 负责人: Estelle Marla Everett
• 金额: $ 17.38万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-19 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10429863
• 关键词: Achievement; Address; Adult; Algorithms; American; Area; Award; Blood Glucose Self-Monitoring; Blood Vessels; Bypass; Calibration; Caring; Cessation of life; Clinical; Clinical Trials; Clinical Trials Design; Complex; Computerized Medical Record; County; Data; Data Analyses; Data Collection; Development; Diabetes Mellitus; Documentation; Dose; Electronic Health Record; Endocrinologist; Enrollment; Evaluation; Fingers; Funding; Future; Glycosylated hemoglobin A; Goals; Guidelines; Health Services; Health system; Healthcare Systems; Hybrids; Injections; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Intervention; K-Series Research Career Programs; Knowledge; Los Angeles; Medical Research; Mentored Patient-Oriented Research Career Development Award; Methods; National Institute of Diabetes and Digestive and Kidney Diseases; Natural Language Processing; Outcome; Paper; Patient Care; Patients; Population; Positioning Attribute; Principal Investigator; Publishing; Pump; Quality of life; Randomized Controlled Trials; Research; Research Personnel; Research Training; Risk; Self Management; Series; Socioeconomic Status; System; Technology; Time; Training; Vulnerable Populations; basal insulin; base; care burden; care outcomes; clinical care; design; diabetes control; diabetes management; effectiveness evaluation; efficacy evaluation; ethnic minority; experience; glucose monitor; glycemic control; health care settings; health literacy; high risk; high risk population; improved; improved outcome; interest; low socioeconomic status; minority patient; monitoring device; new technology; novel; patient oriented; patient population; racial and ethnic; real time monitoring; safety net; satisfaction; sensor; skills

Project Summary/Abstract This career development award will establish me (Dr. Estelle Everett MD, MHS), as an independent investigator focused on evaluating and addressing disparities in management and outcomes in vulnerable patient populations with type 1 diabetes (T1D). This K23 award will provide the support I need to develop expertise in three key areas: 1) EMR-based retrospective data analysis and natural language processing (NLP) methods, 2) clinical trial design, implementation, and analysis, and 3) qualitative study development, data collection and analysis. After receiving my medical and research training in at Johns Hopkins and publishing a series of general diabetes papers, my research interests have increasingly focused on patients with poorly controlled T1D. I am committed to improving care and outcomes in this complex group of patients whose long duration of T1D increases their risk of developing complications early in their lifetime. Although diabetes technology has revolutionized T1D management, disparities in technology access are evident among racial-ethnic minorities, patients with lower socioeconomic status and those with poorly controlled T1D. To help address these gaps, it is critical to leverage electronic health record (EHR) data to readily identify patients experiencing diabetes technology disparities. In order to examine whether diabetes technology can reduce diabetes care burdens and enhance outcomes among some of highest need patients, we need to expand diabetes technology clinical trials beyond the very select populations included thus far (ie., mostly White, higher SES). Therefore, I propose to: 1) To develop and validate a novel electronic medical record (EMR) algorithm using natural language processing (NLP) to identify insulin pump and/or CGM use among patients with type 1 diabetes; 2) To perform a pilot RCT of hybrid closed-loop insulin pump therapy (HCL) in 40 diverse adult patients with poorly controlled T1D (HbA1c >9%) from the largest academic and safety net health systems in the Los Angeles region; and 3) To identify facilitators and barriers of effective use of closed loop insulin pump therapy in patients with poorly controlled T1D. Findings from Aim 1 can be readily used to support T1D care in other settings and findings from Aims 2 and 3 will also be used to inform a future RCT as part of a future NIDDK R01 application. This K23 award will provide the support to complete these aims and my educational objectives, which will provide me with the training and skills needed to become a national leader and independent clinician-investigator aimed to improve outcomes in vulnerable populations with T1D.

项目概要/摘要 这个职业发展奖将使我（Estelle Everett 博士，医学博士，MHS）成为一名独立的 研究人员专注于评估和解决弱势群体管理和结果方面的差异 1 型糖尿病 (T1D) 患者群体。这个 K23 奖项将为我提供发展所需的支持 三个关键领域的专业知识：1）基于 EMR 的回顾性数据分析和自然语言处理 (NLP) 方法，2) 临床试验设计、实施和分析，以及 3) 定性研究开发， 数据收集和分析。 在约翰·霍普金斯大学接受医学和研究培训并出版了一系列一般性文章后 随着糖尿病论文的发表，我的研究兴趣越来越集中于控制不佳的 T1D 患者。我 我致力于改善这一复杂的 1 型糖尿病病程较长的患者群体的护理和治疗结果 增加了他们在生命早期出现并发症的风险。尽管糖尿病技术已 彻底改变了 T1D 管理，少数族裔之间在技术获取方面存在明显差异， 社会经济地位较低的患者和 T1D 控制不佳的患者。 为了帮助解决这些差距，利用电子健康记录 (EHR) 数据来轻松识别至关重要 经历糖尿病技术差异的患者。为了检验糖尿病技术是否可以 减轻糖尿病护理负担并改善一些最需要的患者的治疗结果，我们需要 将糖尿病技术临床试验扩展到迄今为止所包括的特定人群之外（即，大多数 白色，SES 较高）。因此，我建议：1）开发并验证新型电子病历 (EMR) 算法使用自然语言处理 (NLP) 来识别胰岛素泵和/或 CGM 的使用情况 1型糖尿病患者； 2) 在以下国家进行混合闭环胰岛素泵疗法 (HCL) 的试点随机对照试验 来自最大学术和安全网的 40 名不同的 T1D 控制不佳 (HbA1c > 9%) 成年患者 洛杉矶地区的卫生系统； 3) 识别有效利用的促进因素和障碍 闭环胰岛素泵治疗控制不佳的 T1D 患者。目标 1 的发现很容易 用于支持其他环境下的 T1D 护理，目标 2 和 3 的发现也将用于为未来提供信息 RCT 作为未来 NIDDK R01 应用程序的一部分。这个 K23 奖项将为完成这些提供支持 目的和我的教育目标，这将为我提供成为一名 国家领导人和独立临床研究者旨在改善弱势群体的治疗结果 患有 T1D。