Development and validation of a hypoglycemia risk prediction tool for hospitalized patients with type 2 diabetes mellitus treated with insulin

Abstract

BACKGROUND

Insulin is the preferred clinical treatment for hospitalized patients with type 2 diabetes mellitus (T2DM) to control blood glucose effectively. Hypoglycemia is one of the most common adverse events. Accurate prediction of the risk of hypoglycemia is critical in reducing hypoglycemic events and related adverse events in hospitalized diabetic patients treated with insulin.

AIM

To develop and validate a hypoglycemia risk prediction tool for hospitalized patients with T2DM treated with insulin.

METHODS

This retrospective study included 802 hospitalized patients with T2DM in the Department of Endocrinology, the Third Affiliated Hospital of Sun Yat-sen University, between January 2021 and December 2021. The hypoglycemia risk prediction model was developed using logistic regression and nomogram models. The model was validated and calibrated using receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test.

RESULTS

The incidence of hypoglycemia among the enrolled patients was 44.9%. The hypoglycemic risk prediction model included six predictors: Body mass index, duration of diabetes, history of hypoglycemia within 1 year, glomerular filtration rate, blood triglyceride levels, and duration of treatment. The hypoglycemia risk prediction model displayed high discrimination ability (area under the curve = 0.67) and good calibration power (goodness of fit, χ² =12.25, P = 0.14).

CONCLUSION

The hypoglycemia risk prediction model for hospitalized patients with T2DM on insulin therapy displayed high reliability and discrimination ability. The model is a promising tool for clinicians to screen hospitalized patients with T2DM and an elevated risk of hypoglycemia and guide personalized interventions to prevent and treat hypoglycemia.

Keywords: Hypoglycemia; Risk prediction tool; Type 2 diabetes mellitus; Insulin

Core Tip: The hypoglycemia risk-prediction model was developed using the logistic regression and nomogram models. The model was validated and calibrated using the receiver operating characteristic curves and the Hosmer-Lemeshow goodness of fit test. The incidence of hypoglycemia was 44.9%. The model included eight independent hypoglycemia risk factors. The hypoglycemia risk prediction model for hospitalized T2 diabetes mellitus patients treated with insulin showed high reliability and discrimination ability.