Christoffer Sejling


MSc in Mathematical Statistics
PhD @ University of Copenhagen


New Methods for Functional Data to quantify Clinically Relevant Traits of CGM (Continuous Glucose Monitor) Measurement Patterns and guide Clinical Decision Making

Diabetes and prediabetes are increasingly prevalent conditions in modern society, both of which are associated with numerous health hazardous conditions such as obesity, hypertension, and cardiovascular disease. In itself, type 1 diabetes (T1D) is a life changing diagnosis, forcing a need for constant health awareness. When dealing with these challenges, a continuous glucose monitor (CGM) is a vital tool that helps patients evaluate their own health and helps inform clinical decision making in a cost effective manner. Use of CGM devices is therefore becoming more and more common in diabetes clinics around the world, where data from CGMs are collected and analyzed with the objective of optimizing patient care. The increasing adoption of CGMs brings about a huge potential for improving care by developing a data-driven methodology that can be used to assess the CGM data. However, since only simplistic methods based on different summary statistics have been attempted in clinical practice, we still need to uncover the full potential of the information production in CGM measurements.

In this project, we aim at further developing the statistical methodology for drawing out information from CGM trajectories by making use of complex features such as slope, locality, and temporality. In particular we seek to carry out prediction and statistical inference for clinically relevant outcomes on that basis. Additionally, we aim at estimating causal effects, which may help guide clinical decision making. As outcomes, we consider the occurrence of entering and leaving a state of remission as well as the occurrence of entering a state of hypoglycemia for T1D patients at Steno Diabetes Center Copenhagen. We specifically seek to enhance performance in the prediction of these clinical occurrences and the identification of clinically meaningful attributes by taking advantage of the longitudinal calendar order of the observed CGM trajectories for each patient.

In summary, we aim at obtaining a characterization of CGM trajectory shapes that provides accessible, usable, and valid information, on which clinicians may base their assessments and decisions.