Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study

Abstract

Background:Patients with diabetes rely on blood glucose (BG) monitoring devices to manage their condition. As some self-monitoring devices are becoming more and more accurate, it becomes critical to understand the relationship between system accuracy and clinical outcomes, and the potential benefits of analytical accuracy.Methods:We conducted a 30-day in-silico study in type 1 diabetes mellitus (T1DM) patients using continuous subcutaneous insulin infusion (CSII) therapy and a variety of BG meters, using the FDA-approved University of Virginia (UVA)/Padova Type 1 Simulator. We used simulated meter models derived from the published characteristics of 43 commercial meters. By controlling random events in each parallel run, we isolated the differences in clinical performance that are directly associated with the meter characteristics.Results:A meter’s systematic bias has a significant and inverse effect on HbA1c (P < .01), while also affecting the number of severe hypoglycemia events. On the other hand, error, defined as the fraction of measurements beyond 5% of the true value, is a predictor of severe hypoglycemia events (P < .01), but in the absence of bias has a nonsignificant effect on average glycemia (HbA1c). Both bias and error have significant effects on total daily insulin (TDI) and the number of necessary glucose measurements per day (P < .01). Furthermore, these relationships can be accurately modeled using linear regression on meter bias and error.Conclusions:Two components of meter accuracy, bias and error, clearly affect clinical outcomes. While error has little effect on HbA1c, it tends to increase episodes of severe hypoglycemia. Meter bias has significant effects on all considered metrics: a positive systemic bias will reduce HbA1c, but increase the number of severe hypoglycemia attacks, TDI use, and number of fingersticks per day.

The Financial Impact of Inaccurate Blood Glucose Monitoring Systems

“…Our previous analysis demonstrated that inaccurate BGM systems can result in significant increases in HbA1c, severe hypoglycemia and health care resource utilization. 21 We also observed that system bias can have an effect on these metrics. Whereas a systemic negative bias will slightly raise HbA1c, a positive systemic bias will reduce HbA1c but increase the number of severe hypoglycemia episodes, total daily insulin use, and number of fingersticks per day.…”

“…In our analysis, we utilized findings from a recent study by Campos-Náñez and colleagues, who used the UVA/PADOVA Type 1 Diabetes Simulator to assess the effect of BGM system accuracy on clinical outcomes, including changes in HbA1c, SHE, insulin utilization, and SMBG frequency. 21 …”

“…In our recent analysis, we designed a 30-day scenario based on behavioral models, which were used to generate the glycemic response for each in silico adult T1DM subject (n = 100) in the simulator, using each glucose meter modeled in their database. 21 The database included 43 blood glucose meters, previously assessed for accuracy by Freckmann and colleagues, 24 and an “ideal” (error-free) meter as a reference. Thirty replicates of each subject / glucose meter combination were simulated, resulting in more than 10 000 simulated patient years.…”

“…In summary, we found that BGM systems compliant with current accuracy requirements (DIN EN ISO 15197:2013) have only limited impact on HbA1c, SHE, insulin utilization, and SMBG frequency, whereas systems not meeting the standards can have significant clinical influence on one or more of these outcomes. 21 …”

“…Although the clinical impact of the inaccurate BGM systems has yet to be demonstrated in human trials, a recent in silico study of T1DM patients addressed this question in a study that utilized the UVA-PADOVA Type 1 Diabetes Simulator to assess the effect of BGM system accuracy on clinical outcomes. 21 In this analysis, we apply findings from the study to assess the financial impact of inaccuracy of BGM devices on changes in HbA1c, annual SHE, and health care resource utilization (insulin and test strip consumption), using the HbA1c Translator model. 22 …”