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

Abstract

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. 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. 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. 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.