Performance of three different continuous glucose monitoring systems in children with type 1 diabetes during a diabetes summer camp.
Katrin NaglGabriele BergerFelix AbererHaris ZikoKatharina WeimannIna BozicBirgit M Rami-MerharJulia K MaderPublished in: Pediatric diabetes (2020)
The aim of this study was to assess accuracy of the three most commonly used continuous glucose monitoring (CGM) systems in almost real-life situation during a diabetes camp in children with type 1 diabetes (T1D) aged 9-14 years. Data was gathered during a 2-week summer camp under physicians' supervision. Out of 38 participating children with T1D (aged: 11.0 [9.9; 12.1] years; 57% girls, mean HbA1c 7.2 [6.9; 7.7] %,) 37 wore a CGM system (either Abbott FreeStyle Libre (FSL), Dexcom G6 (DEX) or Medtronic Enlite (ENL)) throughout the camp. All concomitantly available data pairs of capillary glucose measurements and sensor values were used for the analysis. Mean absolute relative difference (MARD) was calculated and Parkes Error Grid analyses were done for all three systems used. In total 2079 data pairs were available for analysis. The overall MARDs of CGM systems used at the camp was FSL: 13.3% (6.7;21.6). DEX: 10.3% (5.8; 16.7) and ENL 8.5% (3.6; 15.6). During eu-, hypo- and hyperglycemia MARDs were lowest in ENL. Highest MARDs were seen in hypoglycemia, where all three systems exhibited MARDs above 15%. Overnight MARDs of all systems was higher than during daytime. All sensors performed worst in hypoglycemia. Performance of the adequately calibrated Medtronic system outperformed the factory-calibrated sensors. For clinical practice, it is important to adequately train children with T1D and families in the correct procedures for sensors that require calibrations.
Keyphrases
- type diabetes
- young adults
- binding protein
- glycemic control
- cardiovascular disease
- electronic health record
- clinical practice
- primary care
- big data
- low cost
- randomized controlled trial
- machine learning
- metabolic syndrome
- obstructive sleep apnea
- oxidative stress
- mass spectrometry
- heat stress
- high resolution
- depressive symptoms
- deep learning