Reference : Continuous glucose monitoring in newborn infants: how do errors in calibration measur...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/196582
Continuous glucose monitoring in newborn infants: how do errors in calibration measurements affect detected hypoglycemia?
English
Thomas, Felicity Louise mailto [Université de Liège - ULiège > > > Doct. sc. ingé. & techno. (électr., électro. & inf- paysage)]
Signal, Mathew [> >]
Harris, Deborah L. [> >]
Weston, Philip J. [> >]
Harding, Jane E. [> >]
Shaw, Geoffrey M. [> >]
Chase, J. Geoffrey [> >]
2014
Journal of Diabetes Science and Technology
8
3
543-50
Yes (verified by ORBi)
International
1932-2968
1932-2968
United States
[en] Algorithms ; Biomarkers/blood ; Blood Glucose/metabolism ; Calibration ; Computer Simulation ; Equipment Design ; Humans ; Hypoglycemia/blood/diagnosis ; Infant, Newborn ; Monitoring, Physiologic/instrumentation/methods/standards ; Monte Carlo Method ; Predictive Value of Tests ; Reference Standards ; Reproducibility of Results ; Time Factors ; algorithm ; continuous glucose monitor ; hypoglycemia ; intensive care unit ; neonatal ; recalibration
[en] Neonatal hypoglycemia is common and can cause serious brain injury. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing blood glucose (BG) measurements. Calibration algorithms use BG measurements to convert sensor signals into CGM data. Thus, inaccuracies in calibration BG measurements directly affect CGM values and any metrics calculated from them. The aim was to quantify the effect of timing delays and calibration BG measurement errors on hypoglycemia metrics in newborn infants. Data from 155 babies were used. Two timing and 3 BG meter error models (Abbott Optium Xceed, Roche Accu-Chek Inform II, Nova Statstrip) were created using empirical data. Monte-Carlo methods were employed, and each simulation was run 1000 times. Each set of patient data in each simulation had randomly selected timing and/or measurement error added to BG measurements before CGM data were calibrated. The number of hypoglycemic events, duration of hypoglycemia, and hypoglycemic index were then calculated using the CGM data and compared to baseline values. Timing error alone had little effect on hypoglycemia metrics, but measurement error caused substantial variation. Abbott results underreported the number of hypoglycemic events by up to 8 and Roche overreported by up to 4 where the original number reported was 2. Nova results were closest to baseline. Similar trends were observed in the other hypoglycemia metrics. Errors in blood glucose concentration measurements used for calibration of CGM devices can have a clinically important impact on detection of hypoglycemia. If CGM devices are going to be used for assessing hypoglycemia it is important to understand of the impact of these errors on CGM data.
http://hdl.handle.net/2268/196582
(c) 2014 Diabetes Technology Society.

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