title: Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring.
abstract: BACKGROUND
  The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes.
  
  
  METHODS
  A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to "replay" real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows.
  
  
  RESULTS
  In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels ≤50 mg/dL and BG levels ≤39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD =10%. Similarly, hyperglycemia (frequency of BG levels ≥250 mg/dL and BG levels ≥400 mg/dL) increased and displayed an abrupt slope change at MARD=10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively.
  
  
  CONCLUSIONS
  Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD=10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.
authors.affiliation: 1 University of Virginia Center for Diabetes Technology , Charlottesville, Virginia.
authors.forename: Boris P
authors.initials: BP
authors.lastname: Kovatchev
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Patek
authors.forename: Edward Andrew
authors.initials: EA
authors.lastname: Ortiz
authors.forename: Marc D
authors.initials: MD
authors.lastname: Breton
mesh_terms: D000328:Adult; D000368:Aged; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000379:methods / Q000592:standards*; D003198:Computer Simulation / Q000706:statistics & numerical data; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000097:blood* / Q000188:drug therapy; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000175:diagnosis; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis; D007004:Hypoglycemic Agents / Q000008:administration & dosage; D007328:Insulin / Q000008:administration & dosage; D007332:Insulin Infusion Systems; D008297:Male; D062407:Meals; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin