PloS one
Empirical transition probability modeling of continuous glucose monitoring data
By Yu Y, Banker M, Sherr J and 1 others.
Abstract
Introduction
Continuous glucose monitoring (CGM) produces rich time series data. Standard metrics such as time-in-range summarize glycemia but miss the sequence of changes. We aimed to characterize glucose dynamics using empirical transition probability techniques.
Materials and methods
Using publicly available data for 244 participants who enrolled in the Insulin-Only Bionic Pancreas (IOBP2) randomized clinical trial, we mapped each CGM reading to one of five states: < 54, 54-69, 70-180, 181-250, or >250 mg/dL. We counted state changes between consecutive readings collected every 5 minutes. For each participant, we built a 5 × 5 transition probability matrix and averaged matrices within comparator groups. Statistical inference using estimated transition probability matrices was compared to inferences based on standard metrics including mean glucose, standard deviation, coefficient of variation, time-in-range metrics, Glycemia Risk Index (GRI), Mean of Daily Differences (MODD), and Mean Amplitude of Glycemic Excursions (MAGE).