Development and Validation of a Risk Prediction Model for Severe Hypoglycemia in Adult Patients with Type 2 Diabetes: a Nationwide Population-based Cohort Study

Purpose: There is a scarcity of long-term prediction models for severe hypoglycemia (SH) in subjects with type 2 diabetes mellitus (T2DM). In this study, a model was developed and validated to predict the risk of SH in adult patients with T2DM.

Patients And Methods: Baseline and follow-up data from patients with T2DM who received health evaluations from January 1, 2009, to December 31, 2010 (n=1,676,885) were analyzed as development (n=1,173,820) and validation (n=503,065) cohorts using the National Health Insurance Database (DB) in Korea. New SH episodes were identified using ICD-10 codes. A Cox proportional hazards regression model and Cox model coefficients were used to derive a risk scoring system, and 14 predictive variables were selected. A risk score nomogram based on the risk prediction model was created to estimate the 1-year risk of SH.

Results: In the development cohort, a total of 5,325 (0.45%) patients experienced SH episodes during the follow-up period. After multivariable adjustment, older age, female sex, current smoker, drinking, low body mass index, lack of exercise, previous SH events, insulin or multiple oral hypoglycemic agent use, presence of hypertension or chronic kidney disease, longer duration of diabetes, low or high glucose level, and high Charlson Comorbidity Index score were found to be significant risk factors for the development of SH and were incorporated into the risk model. The concordance indices were 0.871 (95% confidence interval, 0.863-0.881) in development cohort and 0.866 (95% CI, 0.856-0.879) in the validation cohort. The calibration plot showed a nearly 45° line, which indicates that this model predicts well an absolute SH event.

Conclusion: This 14-variable prediction model for SH events may be a useful tool to identify high-risk patients and guide prevention of SH in adult patients with T2DM.