Evaluation of the utility of a glycemic pattern identification system

Abstract

Background: With the increasing prevalence of systems allowing automated, real-time transmission of blood glucose data there is a need for pattern recognition techniques that can inform of deleterious patterns in glycemic control when people test. We evaluated the utility of pattern identification with a novel pattern identification system named Vigilant™ and compared it to standard pattern identification methods in diabetes.

Method: To characterize the importance of an identified pattern we evaluated the relative risk of future hypoglycemic and hyperglycemic events in diurnal periods following identification of a pattern in a data set of 536 patients with diabetes. We evaluated events 2 days, 7 days, 30 days, and 61-90 days from pattern identification, across diabetes types and cohorts of glycemic control, and also compared the system to 6 pattern identification methods consisting of deleterious event counts and percentages over 5-, 14-, and 30-day windows.

Results: Episodes of hypoglycemia, hyperglycemia, severe hypoglycemia, and severe hyperglycemia were 120%, 46%, 123%, and 76% more likely after pattern identification, respectively, compared to periods when no pattern was identified. The system was also significantly more predictive of deleterious events than other pattern identification methods evaluated, and was persistently predictive up to 3 months after pattern identification.

Conclusions: The system identified patterns that are significantly predictive of deleterious glycemic events, and more so relative to many pattern identification methods used in diabetes management today. Further study will inform how improved pattern identification can lead to improved glycemic control.

Keywords: analysis; blood; diabetes; glucose; identification; pattern.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Erik A. Otto is an employee and equity holder of InSpark Technologies. Vinay Tannan is a consultant to InSpark Technologies and receives compensation for services rendered.

© 2014 Diabetes Technology Society.

Figures

Figure 1.

(A) Increased likelihood of hyperglycemic and hypoglycemic events in the same diurnal time period following a method 1 pattern message versus no pattern message, over a subsequent 7-day window. (B) Increased likelihood of severe hyperglycemic and severe hypoglycemic events in the same diurnal time period following a method 1 pattern message versus no message, over a subsequent 7-day window. Black bars indicate proportion of events following a method 1 message; gray bars indicate proportion of events when no message is given. *Indicates a statistically significant difference (P < .001) using the Wilcoxon signed rank test.

Figure 2.

(A) The relative risk of subsequent deleterious events for method 1 when compared to the mean of 6 methods of identifying patterns for hyperglycemic and hypoglycemic events across windows of 2 days, 7 days, 30 days, and 61-90 days. (B) The relative risk of subsequent deleterious events for the method 1 algorithm when compared to the mean of 6 comparative methods of identifying patterns for severe hyperglycemic and severe hypoglycemic events across windows of 2 days, 7 days, 30 days, and 61-90 days. Black bars indicate the relative risk for the method 1 algorithm; gray bars indicate the relative risk for the mean of the 6 comparative methods of identifying patterns.

Figure 3.

Partial receiver operating characteristic curves for method 1 and the 6 comparative methods for messaging frequencies up to 2 times method 1’s default messaging frequency. (A) Hyperglycemia, (B) hypoglycemia, (C) severe hyperglycemia, and (D) severe hypoglycemia.