Prediction of Severe Hypoglycemia

Abstract

OBJECTIVE—Prevention of severe hypoglycemia (SH) is premised partially on the ability to accurately anticipate its occurrence. This study prospectively tests methods for predicting SH using blood glucose meter readings. RESEARCH DESIGN AND 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-monitored blood glucose (SMBG) readings were stored on and retrieved from memory meters, and participants were queried biweekly about occurrence of SH. Respective demographics for the two groups were age 40.7 and 50.2 years, duration of diabetes 20.0 and 12.2 years, A1C 7.6 and 8.8%, and male sex 43 and 39%, respectively. RESULTS—Relative risk for SH, quantified by the ratio of an individual's low blood glucose index (LBGI) based on the previous 150 SMBG readings to the LBGI based on recent SMBG readings, increased significantly in the 24 h before 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 h) SH episodes in the type 1 diabetic group and 60% of those in the type 2 diabetic group when three SMBG readings were available in the 24 h before an episode. Detection increased to 63 and 75%, respectively, if five SMBG readings were available in the 24 h before an episode. CONCLUSIONS—SH often follows a specific blood glucose 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.