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Improving IV Insulin Administration in a Community Hospital

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"Symptom-based insulin adjustment for glucose normalization" (SIGN) algorithm: a pilot study.

Joyce Yu-Chia Lee¹, Keith Tsou, Jiahui Lim

¹Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore.

Diabetes Technology & Therapeutics

|October 6, 2012

Summary

This summary is machine-generated.

The Symptom-based Insulin adjustment for Glucose Normalization (SIGN) algorithm safely improved HbA1c in type 2 diabetes patients with limited glucose monitoring. This approach offers a viable solution for managing uncontrolled diabetes when self-monitoring of blood glucose records are scarce.

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Area of Science:

Endocrinology
Metabolic Diseases
Clinical Therapeutics

Background:

Limited self-monitoring of blood glucose (SMBG) records pose challenges in managing type 2 diabetes, particularly insulin therapy adjustments.
The Symptom-based Insulin adjustment for Glucose Normalization (SIGN) algorithm was developed to address this clinical gap.
This study evaluated the clinical outcomes and safety of the SIGN algorithm in patients with uncontrolled type 2 diabetes.

Purpose of the Study:

To assess the effectiveness of the SIGN algorithm in improving glycemic control.
To evaluate the safety profile of the SIGN algorithm, focusing on hypoglycemic episodes.
To determine the algorithm's utility in managing type 2 diabetes patients with infrequent SMBG data.

Main Methods:

A prospective study involving 114 patients with uncontrolled type 2 diabetes and limited SMBG use.
Data collection included glycated hemoglobin (HbA1c), insulin usage, and adverse events over 6 months post-algorithm implementation.
Exclusion criteria included type 1 diabetes, nonadherence, and absence of insulin therapy.

Main Results:

Mean HbA1c showed significant improvement: 0.29% at 3 months (P=0.015) and 0.41% at 6 months (P=0.006).
A slight HbA1c increase was noted when the algorithm was not implemented.
No major hypoglycemic episodes occurred; minor episodes were minimal and linked to irregular meal habits.

Conclusions:

The SIGN algorithm provides a practical and safe method for managing type 2 diabetes in patients with minimal SMBG records.
It offers a viable clinical solution for improving glycemic control in challenging patient populations.
The algorithm demonstrates potential for enhancing diabetes care where traditional monitoring is limited.