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Published on: November 13, 2016
A consensus perceived glycemic variability metric.
Cynthia R Marling1, Nigel W Struble, Razvan C Bunescu
1School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA. marling@ohio.edu
Journal of Diabetes Science and Technology
|August 6, 2013
Summary
A new Consensus Perceived Glycemic Variability (CPGV) metric, developed using machine learning, accurately assesses glycemic variability (GV) from continuous glucose monitoring (CGM) data. This metric aids in evaluating diabetes mellitus control.
Area of Science:
- Endocrinology
- Data Science
- Medical Informatics
Background:
- Glycemic variability (GV) is crucial for diabetes mellitus management.
- Current continuous glucose monitoring (CGM) analysis lacks a standardized GV metric.
- Physicians visually assess GV from CGM plots, but subjective interpretation varies.
Purpose of the Study:
- To develop a novel Consensus Perceived Glycemic Variability (CPGV) metric.
- To enable routine application of the CPGV metric to CGM data.
- To objectively assess diabetes mellitus control through GV analysis.
Main Methods:
- Physician consensus on GV levels from 250 CGM plots.
- Machine learning algorithms (multilayer perceptrons and support vector machines for regression) trained on physician ratings.
- 10-fold cross-validation to evaluate model performance using 12 input features.
Main Results:
- Support vector regression (SVR) models outperformed multilayer perceptrons in approximating physician consensus.
- The best SVR model demonstrated comparable performance to individual physicians in matching consensus ratings.
- The CPGV metric achieved 90.1% accuracy, 97.0% sensitivity, and 74.1% specificity in identifying excessive GV, outperforming existing metrics.
Conclusions:
- The developed CPGV metric offers a reliable method for assessing overall glucose control.
- CPGV can be routinely integrated into clinical practice alongside glycosylated hemoglobin.
- This metric enhances the objective evaluation of diabetes management using CGM data.
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