Quality specifications for glucose meters: assessment by simulation modeling of errors in insulin dose.

Abstract

BACKGROUND:

Proposed quality specifications for glucose meters allow results to be in error by 5-10% or more of the "true" concentration. Because meters are used as aids in the adjustment of insulin doses, we aimed to characterize the quantitative effect of meter error on the ability to identify the insulin dose appropriate for the true glucose concentration.

METHODS:

Using Monte Carlo simulation, we generated random "true" glucose values within defined intervals. These values were converted to "measured" glucose values using mathematical models of glucose meters having defined imprecision (CV) and bias. For each combination of bias and imprecision, 10,000-20,000 true and measured glucose concentrations were matched with the corresponding insulin doses specified by selected insulin-dosing regimens. Discrepancies in prescribed doses were counted and their frequencies plotted in relation to bias and imprecision.

RESULTS:

For meters with a total analytical error of 5%, dosage errors occurred in approximately 8-23% of insulin doses. At 10% total error, 16-45% of doses were in error. Large errors of insulin dose (two-step or greater) occurred >5% of the time when the CV and/or bias exceeded 10-15%. Total dosage error rates were affected only slightly by choices of sliding scale among insulin dosage rules or by the range of blood glucose. To provide the intended insulin dosage 95% of the time required that both the bias and the CV of the glucose meter be <1% or <2%, depending on mean glucose concentrations and the rules for insulin dosing.

CONCLUSIONS:

Glucose meters that meet current quality specifications allow a large fraction of administered insulin doses to differ from the intended doses. The effects of such dosage errors on blood glucose and on patient outcomes require study.

• Authors: J C Boyd, D E Bruns
• Journal: Clinical chemistry (Clin Chem) Vol. 47 Issue 2 Pg. 209-14 (Feb 2001) ISSN: 0009-9147 [Print] England
• PMID: 11159768 (Publication Type: Journal Article)

Chemical References

• Blood Glucose
• Insulin

Topics

• Blood Glucose (analysis)
• Computer Simulation
• Humans
• Insulin (administration & dosage)
• Monitoring, Physiologic (instrumentation)
• Monte Carlo Method
• Quality Control