Effect of BGM accuracy on the clinical performance of CGM: an in-silico study
E Campos-Náñez, MD Breton - Journal of diabetes science …, 2017 - journals.sagepub.com
Journal of diabetes science and technology, 2017•journals.sagepub.com
Background: Standard management of type 1 diabetes (T1D) relies on blood glucose
monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to
continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize
BGM for calibration and dosing. The question of how the accuracy of both technologies
interact is still not well understood. Methods: We use a recently developed data-driven
simulation approach to characterize the relationship between CGM and BGM accuracy …
monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to
continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize
BGM for calibration and dosing. The question of how the accuracy of both technologies
interact is still not well understood. Methods: We use a recently developed data-driven
simulation approach to characterize the relationship between CGM and BGM accuracy …
Background
Standard management of type 1 diabetes (T1D) relies on blood glucose monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize BGM for calibration and dosing. The question of how the accuracy of both technologies interact is still not well understood.
Methods
We use a recently developed data-driven simulation approach to characterize the relationship between CGM and BGM accuracy especially how BGM accuracy impacts CGM performance in four different use cases with increasing levels of reliance on twice daily calibrated CGM. Simulations are used to estimate clinical outcomes and isolate CGM and BGM accuracy characteristics that drive performance.
Results
Our results indicate that meter (BGM) accuracy, and more specifically systematic positive or negative bias, has a significant effect on clinical performance (HbA1c and severe hypoglycemia events) in all use-cases generated for twice daily calibrated CGMs. Moreover, CGM sensor accuracy can amplify or mitigate, but not eliminate these effects.
Conclusion
As a system, BGM and CGM and their mode of use (use-case) interact to determine clinical outcomes. Clinical outcomes (eg, HbA1c, severe hypoglycemia, time in range) can be closely approximated by linear relationships with two BGM accuracy characteristics, namely error and bias. In turn, the coefficients of this linear relationship are determined by the use-case and by CGM accuracy (MARD).
Sage Journals以上显示的是最相近的搜索结果。 查看全部搜索结果