SCH: Statistical Foundation and Predictive Modeling for Personalized Diabetes Management: Continuous Glucose Monitoring (CGM), Electronic Health Records (EHR), and Biobanks

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

Continuous glucose monitoring (CGM) allows near-continuous glucose measures throughout the 24-hour cycle. Clinical trials have shown CGM use can improve glycemic control of diabetes patients, such as reducing severe hypoglycemia. The rapid advances in sensor technology, ease of use, and expanded reimbursement dramatically promote CGM usage. However, despite increased CGM adoption, successful utilization of CGM data in routine clinical practice still remains low. Enormous amounts of data produced by CGM device and a lack of clear translational value of CGM summary reports for patients’ long-term benefits hinders its adoption. This severely limits realizing CGM’s full potential for personalized diabetes care. In this project, by integrating CGM data with patients’ health data, including medications, lab measures, and comorbidity conditions, PIs propose to develop a set of new data science methods for building robust and interpretable predictive models for early detection and prevention of short-term and long-term adverse diabetes outcomes. Diabetes disease heterogeneity, risk factor trajectories, and data uncertainty in modern devices will be considered in modeling CGM data. Highly scalable algorithms will further enhance the clinical value of CGM. The project will facilitate more intelligent and automated assistance for diabetes patients and their physicians to achieve optimal diabetes management. By harnessing the collaborative research with clinicians and industry partners, the project has a potential to substantially advance the field of wearable health sensors. The project will also provide numerous interdisciplinary opportunities for professional development of the next generation of statisticians and data scientists, by exposing the involved mentees to state-of-the-art data science techniques for smart health. While CGM captures the dynamic glucose profile and plays an increasing role in clinical practice, their measurements are highly dependent on environmental and behavioral factors and subject to measurement errors. Supplementary to CGM data, electronic health records (EHRs) and biobanks offer additional information to quantify health conditions, disease progression, and the associated time-varying risk factors. These large-scale, multimodal data sources enable prospective studies with a detailed collection of long-term time-dependent exposure information to assess risk factors influencing disease onset. To date, there are no statistical methods that can simultaneously analyze sensor data and disease onset at scale in real-time due to forbidding computational costs. The specific thrusts of the project include robust joint modeling of CGM trajectory, time-varying risk factors, time-to-event data at scale; and recurrent events at scale. Furthermore, the new methodology will be developed for dynamic prediction of adverse outcomes incorporating CGM trajectories, patients’ medical history, and genomic biomarkers. The developed tools and algorithms will be implemented in a form of publicly available software as well as cell phone applications to facilitate the CGM usage. The results of the project are expected to have a profound impact onto health and wellbeing of our society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.