BACKGROUND: Various methodologies have been proposed for analysis of continuous glucose measurements. These methods have mainly focused on the proportion of low or high glucose readings and have not attempted to analyze other dimensions of the data obtained. This study proposes an algorithm for analysis of continuous glucose data including a novel method of assessing glycemic variability. METHODS:Mean blood glucose and mean of daily differences (MODD) assessed the degree that the Continuous Glucose Monitoring System (CGMS, Medtronic MiniMed, Northridge, CA) trace was representative of the 3-month glycemic pattern. Percentages of times in low, normal, and high glucose ranges were used to assess marked glycemic excursion. Continuous overall net glycemic action (CONGA), a novel method developed by the authors, assessed intra-day glycemic variability. These methods were applied to 10 CGMS traces chosen randomly from those completed by children with type 1 diabetes from the Royal Children's Hospital, Melbourne, Victoria, Australia and 10 traces recorded by healthy volunteer controls. RESULTS: The healthy controls had lower values for mean blood glucose, MODD, and CONGA. Patients with diabetes had higher percentages of time spent in high and low glucose ranges. There was no overlap between the CONGA values for patients with diabetes and for controls, and the difference between controls and patients with diabetes increased markedly as the CONGA time period increased. CONCLUSIONS: We advocate an approach to the analysis of CGMS data based upon a hierarchy of relevant clinical questions alluding to the representative nature of the data, the amount of time spent in glycemic excursions, and the degree of glycemic variation. Integrated use of these algorithms distinguishes between various patterns of glycemic control in those with and without diabetes.
RCT Entities:
BACKGROUND: Various methodologies have been proposed for analysis of continuous glucose measurements. These methods have mainly focused on the proportion of low or high glucose readings and have not attempted to analyze other dimensions of the data obtained. This study proposes an algorithm for analysis of continuous glucose data including a novel method of assessing glycemic variability. METHODS: Mean blood glucose and mean of daily differences (MODD) assessed the degree that the Continuous Glucose Monitoring System (CGMS, Medtronic MiniMed, Northridge, CA) trace was representative of the 3-month glycemic pattern. Percentages of times in low, normal, and high glucose ranges were used to assess marked glycemic excursion. Continuous overall net glycemic action (CONGA), a novel method developed by the authors, assessed intra-day glycemic variability. These methods were applied to 10 CGMS traces chosen randomly from those completed by children with type 1 diabetes from the Royal Children's Hospital, Melbourne, Victoria, Australia and 10 traces recorded by healthy volunteer controls. RESULTS: The healthy controls had lower values for mean blood glucose, MODD, and CONGA. Patients with diabetes had higher percentages of time spent in high and low glucose ranges. There was no overlap between the CONGA values for patients with diabetes and for controls, and the difference between controls and patients with diabetes increased markedly as the CONGA time period increased. CONCLUSIONS: We advocate an approach to the analysis of CGMS data based upon a hierarchy of relevant clinical questions alluding to the representative nature of the data, the amount of time spent in glycemic excursions, and the degree of glycemic variation. Integrated use of these algorithms distinguishes between various patterns of glycemic control in those with and without diabetes.
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