Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Abstract EN

Aims.

To develop a simple hypoglycemic prediction model to evaluate the risk of hypoglycemia during hospitalization in patients with type 2 diabetes treated with intensive insulin therapy.

Methods.

We performed a cross-sectional chart review study utilizing the electronic database of the Third Affiliated Hospital of Sun Yat-sen University, and included 257 patients with type 2 diabetes undergoing intensive insulin therapy in the Department of Endocrinology and Metabolism.

Logistic regression analysis was used to derive the clinical prediction rule with hypoglycemia (blood glucose≤3.9 mmol/L) as the main result, and internal verification was performed.

Results.

In the derivation cohort, the incidence of hypoglycemia was 51%.

The final model selected included three variables: fasting insulin, fasting blood glucose, and total treatment time.

The area under the curve (AUC) of this model was 0.666 (95% CI: 0.594–0.738, P<0.001).

Conclusions.

The model’s hypoglycemia prediction and the actual occurrence are in good agreement.

The variable data was easy to obtain and the evaluation method was simple, which could provide a reference for the prevention and treatment of hypoglycemia and screen patients with a high risk of hypoglycemia.