title: Retrofitting Real-Life Dexcom G5 Data.
abstract: We proposed in 2014 a retrofitting algorithm to retrospectively increase the accuracy of continuous glucose monitoring (CGM) data by using some blood glucose (BG) measurements. The method proved effective on Dexcom SEVEN Plus when about 10 highly accurate YSI measurements/session were available. In this study, we test the method on Dexcom G5 sensor in a more realistic setup, where only five capillary BG measurements (self-monitoring blood glucose [SMBG]) per 12 h-session are available. Furthermore, we investigate how accuracy is affected by the number of BG measurements.
  
  
  
  The algorithm was tested in 51 adults and 46 adolescents studied for 7 days with Dexcom G5. Each patient also underwent an ∼12-h hospital admission where frequent SMBG and YSI measurements were collected. First, five SMBGs per 12-h session were used to retrofit the CGM. Then, we varied the number of SMBGs provided to the method from 2 to 10 per 12-h session.
  
  
  
  Retrofitted CGM traces with five SMBGs per 12-h session have lower mean absolute difference than original CGM, reduced from 16.2 to 10.7 mg/dL (P < 0.001) in adults and from 17.6 to 11.5 mg/dL (P < 0.001) in adolescents, and mean absolute relative difference is reduced from 9.0% to 6.4% (P < 0.001) in adults and from 10.3% to 6.8% (P < 0.001) in adolescents. Reducing the number of BG measurements reduces improvement in the accuracy from >30% with 10 SMBGs per 12-h session to <16% with 2 SMBGs/day.
  
  
  
  The retrofitting method retrospectively improves the accuracy of CGM data, even if applied to one of the most accurate CGM sensors currently available on the market.
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Simone Del
authors.initials: SD
authors.lastname: Favero
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000627:therapeutic use; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use; D007332:Insulin Infusion Systems; D008297:Male
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin
keywords: Artificial pancreas; Continuous glucose monitoring; Retrospective sensor processing; Sensor enhancement