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

项目摘要/摘要 这个职业发展奖将确立我(埃斯特尔·埃弗雷特博士，医学博士，医学硕士)作为一个独立的 调查员专注于评估和解决弱势群体在管理和结果方面的差异 1型糖尿病(T1D)患者群体。这个K23奖项将为我提供开发所需的支持 三个关键领域的专长：1)基于电子病历的回溯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)定性研究发展， 数据收集和分析。 我在约翰霍普金斯大学接受了医学和研究培训，并出版了一系列一般 在糖尿病论文中，我的研究兴趣越来越多地集中在T1D控制不佳的患者身上。我 我致力于改善这群长期持续T1D的复杂患者的护理和预后 增加了他们在生命早期出现并发症的风险。尽管糖尿病技术已经 T1D管理发生了革命性变化，少数族裔在技术获取方面的差距很明显， 社会经济地位较低的患者和T1D控制较差的患者。 为了帮助弥补这些差距，关键是利用电子健康记录(EHR)数据来容易地识别 经历糖尿病技术差异的患者。为了检查糖尿病技术是否可以 减轻糖尿病护理负担并改善一些最有需要的患者的预后，我们需要 将糖尿病技术临床试验扩大到目前为止纳入的非常精选的人群(即，主要是 白人，更高的SES)。因此，我建议：1)开发和验证一种新型的电子病历 使用自然语言处理(NLP)的(EMR)算法来识别胰岛素泵和/或CGM的使用 1型糖尿病患者；2)进行混合型闭环式胰岛素泵疗法(HCL)的试点RCT 来自最大的学术和安全网络的40名控制不佳的T1D成人患者(HbA1c和Gt；9%) 洛杉矶地区的卫生系统；以及3)确定有效利用的促进者和障碍 控制不良的T1D患者的胰岛素泵治疗。来自AIM 1的发现可以很容易地 用于支持其他环境中的T1D护理，AIMS 2和3的调查结果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。这项K23奖项将为完成这些任务提供支持 目标和我的教育目标，这将为我提供成为一名 旨在改善弱势人群结局的国家领导者和独立临床医生兼调查者 使用T1D。

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.