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NIHNATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES

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

Active
Institution
UNIVERSITY OF CALIFORNIA LOS ANGELES, CA
Principal investigator
Estelle Marla Everett
Funding
$202K
Duration
2022–2027
Open portfolio

Moonbase score

Still being scored

Abstract

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.

LOADING MOONBASE SCORE

NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESK23Diabetes, Endocrinology and Metabolic Diseases B Study Section[DDK-B]closeddiabetesinsulinprocessing nlpclosed looppumptype diabetesnlp

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