Boyd, James C.

Background: We used simulation modeling to relate glucose meter performance criteria to insulin dosing errors for patients on a moderate glycemic control protocol (glucose target, 110–150 mg/dL) and empirically validated assumptions from simulation models using observed glucose meter and laboratory glucose values obtained nearly simultaneously. Subjects and Methods: The 25,948 glucose values from 1,513 patients on a moderate glycemic control protocol were used to represent the expected distribution of glucose values in this patient population. Simulation models were used to relate glucose meter analytical performance to insulin dosing errors assuming 10%, 15%, or 20% total allowable error (TEa). In addition, 4,017 paired glucose meter and serum laboratory glucose measurements drawn within 5 min of each other were used to generate an empiric dataset to validate simulation model assumptions relating glucose meter performance to insulin dosing errors. Results: Large (three or more category) insulin dosing errors are predicted to occur only under the 20% TEa condition. Two category insulin dosing errors were common (6–20% of all insulin dosing decisions) when 20% TEa was assumed, but frequency decreased to only 0.2% of dosing decisions when 10% TEa was modeled. When insulin dosing error rates were measured empirically by comparing paired glucose meter and laboratory glucose values, insulin dosing error rates were very similar to those predicted for the 20% TEa condition. Conclusions: Both simulation models and empiric data demonstrate that glucose meters that perform at ≥20% TEa allow large insulin dosing errors during a moderate glycemic control protocol.

BACKGROUND Maintaining consistency of results over time is a challenge in laboratory medicine. Lot-to-lot reagent changes are a major threat to consistency of results. METHODS For the period October 2007 through July 2012, we reviewed lot validation data for each new lot of insulin-like growth factor 1 (IGF-1) reagents (Siemens Healthcare Diagnostics) at Mayo Clinic, Rochester, MN, and the University of Virginia, Charlottesville, VA. Analyses of discarded patient samples were used for comparison of lots. For the same period, we determined the distributions of reported patient results for each lot of reagents at the 2 institutions. RESULTS Lot-to-lot validation studies identified no reagent lot as significantly different from the preceding lot. By contrast, significant lot-to-lot changes were seen in the means and medians of 105 668 reported patient IGF-I results during the period. The frequency of increased results increased nearly 2-fold to a high of 17%, without detectable changes in the underlying patient demographics. Retrospective statistical analysis indicated that lot-to-lot comparison protocols were underpowered and that validation studies for this assay required testing >100 samples to achieve 90% power to detect reagent lots that would significantly alter the distributions of patient results. CONCLUSIONS The number of test samples required for adequate lot-to-lot validation protocols is high and may be prohibitively large, especially for low-volume or complex assays. Monitoring of the distributions of patient results has the potential to detect lot-to-lot inconsistencies relatively quickly. We recommend that manufacturers implement remote monitoring of patient results from analyzers in multiple institutions to allow rapid identification of between-lot result inconsistency.

AbstractBackground: Glucose meter analytical performance criteria required for safe and effective management of patients on tight glycemic control (TGC) are not currently defined. We used simulation modeling to relate glucose meter performance characteristics to insulin dosing errors during TGC.Methods: We used 29 920 glucose values from patients on TGC at 1 institution to represent the expected distribution of glucose values during TGC, and we used 2 different simulation models to relate glucose meter analytical performance to insulin dosing error using these 29 920 initial glucose values and assuming 10%, 15%, or 20% total allowable error (TEa) criteria.Results: One-category insulin dosing errors were common under all error conditions. Two-category insulin dosing errors occurred more frequently when either 20% or 15% TEa was assumed compared with 10% total error. Dosing errors of 3 or more categories, those most likely to result in hypoglycemia and thus patient harm, occurred infrequently under all error conditions with the exception of 20% TEa.Conclusions: Glucose meter technologies that operate within a 15% total allowable error tolerance are unlikely to produce large (≥3-category) insulin dosing errors during TGC. Increasing performance to 10% TEa should reduce the frequency of 2-category insulin dosing errors, although additional studies are necessary to determine the clinical impact of such errors during TGC. Current criteria that allow 20% total allowable error in glucose meters may not be optimal for patient management during TGC.