Recently Kilpatrick 20 performed a retrospective analysis of DCCT data, and reported that variability plays only a very small role relative to the average level of blood glucose as reflected in A1c. Hirsch, 12– 14 Monnier, 15 and others 16– 19 have emphasized the evidence suggesting the importance of blood glucose variability in generating oxidative stress and potentially contributing to the development of both macro- and microvascular complications of diabetes. The basic principles underlying such approaches have been defined previously. In addition to tabulating the data and providing graphical and statistical analysis, the computer can provide a clinical decision support system using an artificial intelligence or rule-based expert system to identify and prioritize the most important problems facing the patient. However, downloading data from memory meters and computer analysis of SMBG data is used by only a small fraction of physicians caring for patients with diabetes and for a tiny fraction of patient-physician encounters. Glucose meters equipped with memory and ancillary software for downloading and analysis of these data have been available for more than 20 years.
Retrospective analysis of SMBG data is typically performed by inspection of logbook data at the time of ambulatory care visits. 1– 3 SMBG data are typically used by patients to detect or confirm hypo- or hyperglycemia and to take corrective actions in terms of self-adjustment of insulin dosage and timing, adjustments of other medications, or adjustment of the amount, content, and timing of meals. Increasing the frequency of self monitoring of blood glucose (SMBG) has been associated with improvement in the quality of glycemic control in patients with diabetes.
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