No abstract

Six patients with type 1 diabetes participated in a pilot trial. Their median age was 36 years (range 29-61) and the median duration of diabetes was 10 years (range 3-29). They were asked to enter, from their home or work PC, blood glucose values, insulin doses and a food diary. From the data entered, a computer model generated a simulation of the blood glucose concentration for the data collection period. It could then suggest alternative insulin doses (or regimes), or meal sizes, to reduce the risk of hypo- and hyperglycaemia. During a six-month study, patients entered a median of five sets of data (range two to eight). Feedback from participants revealed that while the system was helpful, difficulties with data entry hindered its use. Information gained from this exercise is shaping further development of the system.

Background: An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. Method: Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. Results: With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. Conclusions: The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.