A novel nomogram for predicting subsyndromal delirium in critically ill patients with diabetes
TL;DR
The developed and internally validated a nomogram for predicting subsyndromal delirium (SSD) risk in critically ill patients with diabetes may be useful for risk stratification, but prospective and external validation are required before clinical implementation.
Abstract
Background and aims This study aimed to develop and validate a nomogram for predicting subsyndromal delirium (SSD) risk in critically ill patients with diabetes. Methods Critically ill adult patients with diabetes were retrospectively included in the emergency department of Peking Union Medical College Hospital from January 2025 to December 2025. Univariate and multivariate logistic regression analyses identified independent predictors for SSD. A nomogram was constructed based on these predictors. The model’s performance was assessed by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results A total of 297 patients with diabetes were included and the overall incidence of SSD was 38.7%. Univariate and multivariate analysis identified six independent predictors: higher NRS score, RASS score, APACHE II score, PSQI score, random blood glucose level at admission, and lower Barthel index. The nomogram incorporating these factors demonstrated good discrimination, with an AUC of 0.922 (95% CI: 0.891-0.953). Calibration curves showed good agreement between predicted and observed probabilities, and DCA indicated favorable clinical utility. Conclusions We developed and internally validated a nomogram for predicting SSD in critically ill patients with diabetes. The model may be useful for risk stratification, but prospective and external validation are required before clinical implementation.