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Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes.
Methods Enzymol. 2009; 454:69-86.ME

Abstract

Scientific and industrial effort is now increasingly focused on the development of closed-loop control systems (artificial pancreas) to control glucose metabolism of people with diabetes, particularly type 1 diabetes mellitus. The primary prerequisite to a successful artificial pancreas, and to optimal diabetes control in general, is the continuous glucose monitor (CGM), which measures glucose levels frequently (e.g., every 5 min). Thus, a CGM collects detailed glucose time series, which carry significant information about the dynamics of glucose fluctuations. However, a CGM assesses blood glucose indirectly via subcutaneous determinations. As a result, two types of analytical problems arise for the retrieval and interpretation of CGM data: (1) the order and the timing of CGM readings and (2) sensor errors, time lag, and deviations from BG need to be accounted for. In order to improve the quality of information extracted from CGM data, we suggest several analytical and data visualization methods. These analyses evaluate CGM errors, assess risks associated with glucose variability, quantify glucose system stability, and predict glucose fluctuation. All analyses are illustrated with data collected using MiniMed CGMS (Medtronic, Northridge, CA) and Freestyle Navigator (Abbott Diabetes Care, Alameda, CA). It is important to remember that traditional statistics do not work well with CGM data because consecutive CGM readings are highly interdependent. In conclusion, advanced analysis and visualization of CGM data allow for evaluation of dynamical characteristics of diabetes and reveal clinical information that is inaccessible via standard statistics, which do not take into account the temporal structure of data. The use of such methods has the potential to enable optimal glycemic control in diabetes and, in the future, artificial pancreas systems.

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Authors+Show Affiliations

Kovatchev B
University of Virginia Health System, Charlottesville, Virginia, USA.
Breton M
No affiliation info available
Clarke W
No affiliation info available

MeSH

Blood GlucoseBlood Glucose Self-MonitoringDiabetes MellitusHumansInformation Storage and Retrieval

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

19216923

Citation

Kovatchev, Boris, et al. "Analytical Methods for the Retrieval and Interpretation of Continuous Glucose Monitoring Data in Diabetes." Methods in Enzymology, vol. 454, 2009, pp. 69-86.
Kovatchev B, Breton M, Clarke W. Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes. Methods Enzymol. 2009;454:69-86.
Kovatchev, B., Breton, M., & Clarke, W. (2009). Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes. Methods in Enzymology, 454, 69-86. https://doi.org/10.1016/S0076-6879(08)03803-2
Kovatchev B, Breton M, Clarke W. Analytical Methods for the Retrieval and Interpretation of Continuous Glucose Monitoring Data in Diabetes. Methods Enzymol. 2009;454:69-86. PubMed PMID: 19216923.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Analytical methods for the retrieval and interpretation of continuous glucose monitoring data in diabetes. AU - Kovatchev,Boris, AU - Breton,Marc, AU - Clarke,William, PY - 2009/2/17/entrez PY - 2009/2/17/pubmed PY - 2009/4/7/medline SP - 69 EP - 86 JF - Methods in enzymology JO - Methods Enzymol VL - 454 N2 - Scientific and industrial effort is now increasingly focused on the development of closed-loop control systems (artificial pancreas) to control glucose metabolism of people with diabetes, particularly type 1 diabetes mellitus. The primary prerequisite to a successful artificial pancreas, and to optimal diabetes control in general, is the continuous glucose monitor (CGM), which measures glucose levels frequently (e.g., every 5 min). Thus, a CGM collects detailed glucose time series, which carry significant information about the dynamics of glucose fluctuations. However, a CGM assesses blood glucose indirectly via subcutaneous determinations. As a result, two types of analytical problems arise for the retrieval and interpretation of CGM data: (1) the order and the timing of CGM readings and (2) sensor errors, time lag, and deviations from BG need to be accounted for. In order to improve the quality of information extracted from CGM data, we suggest several analytical and data visualization methods. These analyses evaluate CGM errors, assess risks associated with glucose variability, quantify glucose system stability, and predict glucose fluctuation. All analyses are illustrated with data collected using MiniMed CGMS (Medtronic, Northridge, CA) and Freestyle Navigator (Abbott Diabetes Care, Alameda, CA). It is important to remember that traditional statistics do not work well with CGM data because consecutive CGM readings are highly interdependent. In conclusion, advanced analysis and visualization of CGM data allow for evaluation of dynamical characteristics of diabetes and reveal clinical information that is inaccessible via standard statistics, which do not take into account the temporal structure of data. The use of such methods has the potential to enable optimal glycemic control in diabetes and, in the future, artificial pancreas systems. SN - 1557-7988 UR - https://www.unboundmedicine.com/medline/citation/19216923/Analytical_methods_for_the_retrieval_and_interpretation_of_continuous_glucose_monitoring_data_in_diabetes_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0076-6879(08)03803-2 DB - PRIME DP - Unbound Medicine ER -