Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning

Yinan Mao; Kyle Xin Quan Tan; Augustin Seng; Peter Wong; Sue-Anne Toh; Alex R Cook

doi:10.34133/2022/9892340

Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning

Health Data Sci. 2022 Apr 27:2022:9892340. doi: 10.34133/2022/9892340. eCollection 2022.

Authors

Yinan Mao^{1

2}, Kyle Xin Quan Tan³, Augustin Seng³, Peter Wong³, Sue-Anne Toh^{3

4}, Alex R Cook^{1

2

4

5}

Affiliations

¹ Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
² Department of Statistics and Data Science, National University of Singapore, Singapore.
³ NOVI Health, Singapore.
⁴ Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
⁵ Duke-NUS Medical School, Singapore.

Abstract

Background. Continuous glucose monitoring (CGM) offers an opportunity for patients with diabetes to modify their lifestyle to better manage their condition and for clinicians to provide personalized healthcare and lifestyle advice. However, analytic tools are needed to standardize and analyze the rich data that emerge from CGM devices. This would allow glucotypes of patients to be identified to aid clinical decision-making.Methods. In this paper, we develop an analysis pipeline for CGM data and apply it to 148 diabetic patients with a total of 8632 days of follow up. The pipeline projects CGM data to a lower-dimensional space of features representing centrality, spread, size, and duration of glycemic excursions and the circadian cycle. We then use principal components analysis and $k$ -means to cluster patients' records into one of four glucotypes and analyze cluster membership using multinomial logistic regression.Results. Glucotypes differ in the degree of control, amount of time spent in range, and on the presence and timing of hyper- and hypoglycemia. Patients on the program had statistically significant improvements in their glucose levels.Conclusions. This pipeline provides a fast automatic function to label raw CGM data without manual input.