"Symptom-based Insulin adjustment for Glucose Normalizationa" (SIGN) algorithm: A pilot study

Abstract

Background: Lack of self-monitoring of blood glucose (SMBG) records in actual practice settings continues to create therapeutic challenges for clinicians, especially in adjusting insulin therapy. In order to overcome this clinical obstacle, a "Symptom-based Insulin adjustment for Glucose Normalizationa" (SIGN) algorithm was developed to guide clinicians in caring for patients with uncontrolled type 2 diabetes who have few to no SMBG records. This study examined the clinical outcome and safety of the SIGN algorithm. Subjects and Methods: Glycated hemoglobin (HbA1c), insulin usage, and insulin-related adverse effects of a total of 114 patients with uncontrolled type 2 diabetes who refused to use SMBG or performed SMBG once a day for less than three times per week were studied 3 months prior to the implementation of the algorithm and prospectively at every 3-month interval for a total of 6 months after the algorithm implementation. Patients with type 1 diabetes, nonadherence to diabetes medications, or who were not on insulin therapy at any time during the study period were excluded from this study. Results: Mean HbA1c improved by 0.29% at 3 months (P=0.015) and 0.41% at 6 months (P=0.006) after algorithm implementation. A slight increase in HbA1c was observed when the algorithm was not implemented. There were no major hypoglycemic episodes. The number of minor hypoglycemic episodes was minimal with the majority of the cases due to irregular meal habits. Conclusions: The SIGN algorithm appeared to offer a viable and safe approach when managing uncontrolled patients with type 2 diabetes who have few to no SMBG records. © Mary Ann Liebert, Inc.