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Using stochastic modelling to identify unusual continuous glucose monitor measurements and behaviour, in newborn
Matthew Signal1, Aaron Le Compte, Deborah L Harris
1Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
Insights
A new stochastic model helps clinicians identify unusual Continuous Glucose Monitoring (CGM) data, distinguishing true glycaemic events from sensor noise in infants. This tool aids in managing blood glucose abnormalities and improving patient care.
Area of Science:
- Neonatal medicine
- Biomedical engineering
- Data science
Background:
- Abnormal blood glucose (BG) levels are linked to increased mortality in critically ill infants.
- Hypoglycaemia and high glycaemic variability independently increase mortality risk.
- Continuous Glucose Monitoring (CGM) can detect glycaemic abnormalities, but sensor noise requires management.
Purpose of the Study:
- To develop a tool for clinicians to identify unusual CGM behavior.
- To highlight CGM data requiring careful interpretation.
- To improve the clinical utility of CGM devices.
Main Methods:
- A stochastic model using kernel density estimation was developed based on CGM and BG data from 50 high-risk infants.
- Unusual CGM measurements were classified using historical cohort data.
- A 5-fold Monte Carlo cross-validation was performed to assess model fit.
Main Results:
- The model utilized ~67,000 CGM measurements across a 2-10 mmol/L glycaemic range.
- Validation showed high accuracy: 80% CI captured 83% of data, 90% CI captured 91%, and 99% CI captured 99%.
- The method successfully identified stable, hypoglycaemic, and highly variable glycaemic patterns in patient examples.
Conclusions:
- A stochastic model and classification method effectively highlight unusual CGM behavior.
- The tool can differentiate true glycaemic events from sensor noise and detect sensor degradation.
- Color-coded CGM traces provide rapid, efficient data interpretation for real-time or retrospective analysis.
Background:
Abnormal blood glucose (BG) concentrations have been associated with increased morbidity and mortality in both critically ill adults and infants. Furthermore, hypoglycaemia and glycaemic variability have both been independently linked to mortality in these patients. Continuous Glucose Monitoring (CGM) devices have the potential to improve detection and diagnosis of these glycaemic abnormalities. However, sensor noise is a trade-off of the high measurement rate and must be managed effectively if CGMs are going to be used to monitor, diagnose and potentially help treat glycaemic abnormalities.
Aim:
To develop a tool that will aid clinicians in identifying unusual CGM behaviour and highlight CGM data that potentially need to be interpreted with care.
Methods:
CGM data and BG measurements from 50 infants at risk of hypoglycaemia were used. Unusual CGM measurements were classified using a stochastic model based on the kernel density method and historical CGM measurements from the cohort. CGM traces were colour coded with very unusual measurements coloured red, highlighting areas to be interpreted with care. A 5-fold validation of the model was Monte Carlo simulated 25 times to ensure an adequate model fit.
Results:
The stochastic model was generated using ~67,000 CGM measurements, spread across the glycaemic range ~2-10 mmol/L. A 5-fold validation showed a good model fit: the model 80% confidence interval (CI) captured 83% of clinical CGM data, the model 90% CI captured 91% of clinical CGM data, and the model 99% CI captured 99% of clinical CGM data. Three patient examples show the stochastic classification method in use with 1) A stable, low variability patient which shows no unusual CGM measurements, 2) A patient with a very sudden, short hypoglycaemic event (classified as unusual), and, 3) A patient with very high, potentially un-physiological, glycaemic variability after day 3 of monitoring (classified as very unusual).
Conclusions:
This study has produced a stochastic model and classification method capable of highlighting unusual CGM behaviour. This method has the potential to classify important glycaemic events (e.g. hypoglycaemia) as true clinical events or sensor noise, and to help identify possible sensor degradation. Colour coded CGM traces convey the information quickly and efficiently, while remaining computationally light enough to be used retrospectively or in real-time.
