01 June 2010
Enhanced Algorithm for glucose estimation using the Continuous Glucose Monitoring SystemYenny LealCDEF, Winston Garcia-GabinCDEF, Jorge BondiaAEG, Eduardo EsteveAB, Wifredo RicartAB, Jose-Manuel Fernández-Real, Josep VehíAEG
Med Sci Monit 2010; 16(6): MT51-58 :: ID: 880595
Background: The CGMS® Gold continuous glucose monitor presents a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false alarms. The new algorithm aims to improve the measurement accuracy and hypoglycemia detection.
Material/Methods: Twenty-one patients with type 1 diabetes were monitored for 3 days (1 day at the hospital and 2 at home) using the CGMS Gold. For these patients, blood glucose samples were taken every 15 minutes for 2 hours after meals and every half hour otherwise during the first day. A new calibration algorithm was developed and implemented using CGMS Gold intensity readings and capillary glucose.
Results: After 1 day, a comparison of results from either the CGMS Gold algorithm and the proposed algorithm, compared with results from blood (2450 points), showed an increase of data in zone A with the proposed algorithm (4.4% in the Clarke error grid analysis (EGA) and 5.0% in the Consensus EGA). After comparing for 3 days, a reduction of 24.7%, p<0.05, in the overall median relative absolute difference (RAD) was also obtained. In the hypoglycemic range, a significant decrease in median RAD was observed (64.4%, p<0.05). Furthermore, the undetected hypoglycemia events in capillary samples by the proposed algorithm were reduced by 59.8% compared to the CGMS Gold algorithm.
Conclusions: The performance as measured with clinical and numerical accuracy criteria illustrates the improved accuracy of the proposed algorithm in comparison with the CGMS Gold algorithm. A significant improvement in hypoglycemia detection was observed.
Keywords: Hypoglycemia - pathology, Glucose - metabolism, Gas Chromatography-Mass Spectrometry - methods, Diabetes Mellitus, Type 1 - diagnosis, Calibration, Blood Glucose Self-Monitoring - methods, Algorithms, Adolescent, Models, Biological, Models, Statistical, Reproducibility of Results
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