BACKGROUND: A cause of suboptimal accuracy in amperometric glucose sensors is the presence of a background current (current produced in the absence of glucose) that is not accounted for. We hypothesized that a mathematical correction for the estimated background current of a commercially available sensor would lead to greater accuracy compared to a situation in which we assumed the background current to be zero. We also tested whether increasing the frequency of sensor calibration would improve sensor accuracy. METHODS: This report includes analysis of 20 sensor datasets from seven human subjects with type 1 diabetes. Data were divided into a training set for algorithm development and a validation set on which the algorithm was tested. A range of potential background currents was tested. RESULTS: Use of the background current correction of 4 nA led to a substantial improvement in accuracy (improvement of absolute relative difference or absolute difference of 3.5-5.5 units). An increase in calibration frequency led to a modest accuracy improvement, with an optimum at every 4 h. CONCLUSIONS: Compared to no correction, a correction for the estimated background current of a commercially available glucose sensor led to greater accuracy and better detection of hypoglycemia and hyperglycemia. The accuracy-optimizing scheme presented here can be implemented in real time.
BACKGROUND: A cause of suboptimal accuracy in amperometric glucose sensors is the presence of a background current (current produced in the absence of glucose) that is not accounted for. We hypothesized that a mathematical correction for the estimated background current of a commercially available sensor would lead to greater accuracy compared to a situation in which we assumed the background current to be zero. We also tested whether increasing the frequency of sensor calibration would improve sensor accuracy. METHODS: This report includes analysis of 20 sensor datasets from seven human subjects with type 1 diabetes. Data were divided into a training set for algorithm development and a validation set on which the algorithm was tested. A range of potential background currents was tested. RESULTS: Use of the background current correction of 4 nA led to a substantial improvement in accuracy (improvement of absolute relative difference or absolute difference of 3.5-5.5 units). An increase in calibration frequency led to a modest accuracy improvement, with an optimum at every 4 h. CONCLUSIONS: Compared to no correction, a correction for the estimated background current of a commercially available glucose sensor led to greater accuracy and better detection of hypoglycemia and hyperglycemia. The accuracy-optimizing scheme presented here can be implemented in real time.
Authors: C Choleau; J C Klein; G Reach; B Aussedat; V Demaria-Pesce; G S Wilson; R Gifford; W K Ward Journal: Biosens Bioelectron Date: 2002-08 Impact factor: 10.618
Authors: C Choleau; J C Klein; G Reach; B Aussedat; V Demaria-Pesce; G S Wilson; R Gifford; W K Ward Journal: Biosens Bioelectron Date: 2002-08 Impact factor: 10.618
Authors: D Barry Keenan; Benyamin Grosman; Harry W Clark; Anirban Roy; Stuart A Weinzimer; Rajiv V Shah; John J Mastrototaro Journal: J Diabetes Sci Technol Date: 2011-11-01
Authors: Garry M Steil; Monica Langer; Karen Jaeger; Jamin Alexander; Michael Gaies; Michael S D Agus Journal: Pediatr Crit Care Med Date: 2011-11 Impact factor: 3.624
Authors: Joseph El Youssef; Jessica R Castle; Deborah L Branigan; Ryan G Massoud; Matthew E Breen; Peter G Jacobs; B Wayne Bequette; W Kenneth Ward Journal: J Diabetes Sci Technol Date: 2011-11-01
Authors: Garry M Steil; Jamin Alexander; Alexandra Papas; Langer Monica; Biren P Modi; Hannah Piper; Tom Jaksic; Rebecca Gottlieb; Michael S D Agus Journal: J Diabetes Sci Technol Date: 2011-01-01
Authors: Jessica R Castle; Amy Pitts; Kathryn Hanavan; Rhonda Muhly; Joseph El Youssef; Colleen Hughes-Karvetski; Boris Kovatchev; W Kenneth Ward Journal: Diabetes Care Date: 2012-02-22 Impact factor: 19.112