Development and Validation of Nomogram-Based Predictive Models for Severe Hypoglycemia in Adults With Type 1 Diabetes...

Severe hypoglycemia is a major acute complication of type 1 diabetes (T1D) and is associated with increased morbidity, mortality, and impaired quality of life. Identifying individuals at the highest risk remains essential for optimising preventive strategies in real-world practice.

The SEHYPAN (SEvere HYpoglycemia in ANdalusia) study was a multicenter case-control analysis including adults with T1D treated with multiple daily insulin injections (MDI). Cases were individuals who required pre-hospital emergency care for severe hypoglycemia between 2018 and 2022, each matched by sex, age, glucose-monitoring method (SMBG/isCGM), reference health-care area with controls who had not experienced severe events. Logistic regression models were used to identify independent predictors, and nomograms were generated for individualised risk estimation.

A total of 1464 participants were analysed (799 cases and 665 matched controls). Cases had longer diabetes duration, more comorbidities, and higher rates of smoking and alcohol use (all p < 0.001). Two nomogram-based models were developed: one for the overall cohort, including glucose monitoring modality, history of severe and nocturnal hypoglycemia, comorbid depression, alcohol use, and chronic conditions; and another specific to isCGM users, which also incorporated time in range and time below range. In the full cohort model, isCGM use was independently associated with lower odds of severe hypoglycemia. Both models showed good discrimination (AUC 0.75-0.83) and high sensitivity (≥ 0.75).

The SEHYPAN study identifies key predictors of severe hypoglycemia in adults with T1D on MDI therapy. The innovative nomogram-based models provide personalised risk estimates that may enhance preventive care in everyday clinical practice.