A Simple Method to Model a Continuous Glucose Monitoring Signal. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Thomas, Felicity; Pretty, Christopher; Dickson, Jennifer; Signal, Matthew; Shaw, Geoffrey; Chase, J. Geoffrey
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 8775
• Page End: 8780
• Publication Date: 2017-07
• Subjects: Developments in measurement -- signal processing -- Identification -- validation -- Error quantification -- Time series modelling -- Healthcare management -- disease control -- critical care; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1736
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Before continuous glucose monitoring (CGM) can be safely used to guide glycaemic control (GC) protocols the impact of suboptimal accuracy resulting from error or delay in calibration measurement, sensor drift, and delayed glucose diffusion must first be characterised. Characterising this error allows models to be formed so in-silico simulations can test the performance and safety of CGM driven glycaemic control protocols and examine best and worst scenarios. Existing models of CGM dynamics are now 10 years old and significant advances in sensor technology mean the level of error produced by these models no longer characterises the dynamics of more recent CGM devices. Therefore, this paper presents and validates a simple CGM error model based on the latest available CGM devices, as well as a generalisable sensor modeling approach. The model was created using 28 data sets from an observational pilot study of CGM in patients admitted to the Christchurch Hospital ICU during 2014-15. The model was characterised by empirical models of drift and noise. Autocorrelation was then used to validate the modelled data with the measured data. The median absolute difference between modelled and measured SG autocorrelation values was 0.007 with a range of 0 - 0.13. Hence, the model is judged to be suitable for use in simulation to provide better insight into using CGM to guide GC will effect control and its safety and performance. The overall modelling process is data driven and readily generalised to any other device.