title: Continuous glucose monitoring in subjects with type 1 diabetes: improvement in accuracy by correcting for background current.
abstract: 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.affiliation: Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: El Youssef
authors.forename: Jessica R
authors.initials: JR
authors.lastname: Castle
authors.forename: Julia M
authors.initials: JM
authors.lastname: Engle
authors.forename: Ryan G
authors.initials: RG
authors.lastname: Massoud
authors.forename: W Kenneth
authors.initials: WK
authors.lastname: Ward
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D015374:Biosensing Techniques; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D002138:Calibration; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D005260:Female; D005934:Glucagon / Q000008:administration & dosage / Q000627:therapeutic use; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000175:diagnosis; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis; D007332:Insulin Infusion Systems; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods; D012015:Reference Standards; D015203:Reproducibility of Results
chemical_list: D001786:Blood Glucose; D005934:Glucagon; D006442:Glycated Hemoglobin