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Rodbard D. Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes. J Diabetes Sci Technol. 2007;1(1):62-71doi: 10.1177/193229680700100111.
Rodbard, D. (2007). Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes. Journal of diabetes science and technology, 1(1), 62-71. https://doi.org/10.1177/193229680700100111
Rodbard, David. "Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes." Journal of diabetes science and technology vol. 1,1 (2007): 62-71. doi: https://doi.org/10.1177/193229680700100111
Rodbard D. Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes. J Diabetes Sci Technol. 2007 Jan;1(1):62-71. doi: 10.1177/193229680700100111. PMID: 19888382; PMCID: PMC2769603.
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J Diabetes Sci Technol. 2007 Jan;1(1):62-71. doi: 10.1177/193229680700100111.

Optimizing display, analysis, interpretation and utility of self-monitoring of blood glucose (SMBG) data for management of patients with diabetes.

Journal of diabetes science and technology

David Rodbard

Affiliations

  1. American Institutes for Research, Washington DC 20007, USA. [email protected]

PMID: 19888382 PMCID: PMC2769603 DOI: 10.1177/193229680700100111

Abstract

BACKGROUND: Self-monitoring of blood glucose (SMBG) data have not been used to fullest advantage. Few physicians routinely download data from memory-equipped glucose meters and perform systematic analyses and interpretation of the data. There is need for improved methods for display and analysis of SMBG data, for a systematic approach for identification and prioritization of clinical problems revealed by SMBG, for characterization of blood glucose variability, and for clinical decision support.

METHODS: We have developed a systematic approach to the analysis and interpretation of SMBG data to assist in the management of patients with diabetes. This approach utilizes the following criteria: 1) Overall quality of glycemic control; 2) Hypoglycemia (frequency, severity, timing); 3) Hyperglycemia; 4) Variability; 5) Pattern analysis; and 6) Adequacy of monitoring. The "Pattern analysis" includes assessment of: trends by date and by time of day; relationship of blood glucose to meals; post-prandial excursions; the effects of day of the week, and interactions between time of day and day of the week.

RESULTS: The asymmetrical distribution of blood glucose values makes it difficult to interpret the mean and standard deviation. Use of the median (50(th) percentile) and Inter-Quartile Range (IQR) overcomes these difficulties: IQR is the difference between the 75(th) and 25(th) percentiles. SMBG data can be used to predict the A1c level and indices of the risks of hyperglycemia and hypoglycemia.

CONCLUSION: Given reliable measures of glucose variability, one can apply a strategy to progressively reduce glucose variability and then increase the intensity of therapy so as to reduce median blood glucose and hence A1c, while minimizing the risk of hypoglycemia.

Keywords: clinical decision support; diabetes; glucose; medical informatics; self-monitoring; statistics; variability

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