Prediction of severe hypoglycemia

Abstract

Background: Prevention of severe hypoglycemia (SH) is premised partially on the ability to accurately anticipate its occurrence. This study tests prospectively methods for predicting SH using BG meter readings.

Research Methods: One hundred adults with type 1 diabetes were followed for 6 months and 79 insulin-using adults with type 2 diabetes were followed for 4 months. During this time subjects' routine self-monitoring blood glucose (SMBG) readings were stored on and retrieved from memory meters, and participants were queried bi-weekly about occurrence of SH. Respective demographics for the two groups were: age 40.7/50.2 years, duration of diabetes 20.0/12.2 years, HbA1c 7.6%/8.8%, male gender 43%/39%.

Results: Relative risk for SH, quantified by the ratio of a person's Low BG Index (LBGI) based on the previous 150 SMBG readings to the LBGI based on recent SMBG readings, increased significantly in the 24 hours prior to SH episodes in individuals with type 1 and type 2 diabetes (t=10.3, p<0.0001 and t=4.2, p<0.001 respectively). A sliding algorithm detected 58 % of imminent (within 24 hours) SH episodes in the type 1 group and 60% of those in the type 2 group when 3 SMBG readings were available in the 24 hours before an episode. Detection increased to 63/75% if 5 SMBG readings were available in the 24 hours before an episode.

Conclusion: SH often follows a specific BG fluctuation pattern that is identifiable from SMBG. Thus, partial prediction of imminent SH is possible, providing a potential tool to trigger self-regulatory prevention of significant hypoglycemia.