Development and internal verification of nomogram for forecasting delirium in the elderly admitted to intensive care units: an analysis of MIMIC-IV database - Jiang, Li; Yu, Dongdong; Yang, Ge; Wu, Xiaoqian; Zhang, Dong (Frontiers in Neurology, 2025)
BackgroundPrecise forecasting of delirium in intensive care unit (ICU) may propel effective early prevention strategies and stratification of ICU patients through delirium risks, avoiding waste of medical resources. However, there are few optimal models of delirium in critically ill older patients. This study aimed to propose and verify a nomogram for predicting the incidence of delirium in elderly patients admitted to ICU.MethodsWe performed a retrospective study using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included data on 13,175 older patients in total. The patients were randomly divided into a training group (n = 9,223) and an internal verification group (n = 3,452). Risk factors were screened using the least absolute shrinkage and selection operator regression. We successfully constructed a multivariate logistic regression model along with a nomogram. We conducted internal verification using 1,000 bootstrap specimens. Performance assessment was conducted using a receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).ResultsThe risk factors included in the nomogram were sepsis, Sequential Organ Failure Assessment (SOFA) score, cerebrovascular disease, mechanical ventilation, sedation, severe hypothermia, and serum calcium levels. The area under the ROC curve (AUC) for the nomogram, incorporating the above-mentioned predictors for the training set was 0.762 (95% confidence interval [CI] 0.749–0.776), whereas that for the verification set was 0.756 (95% CI 0.736–0.776). Based on the calibration curve, the model forecast outcomes matched well with the actual results, and the nomogram’s Brier score was 0.12 in the training set and 0.128 in the verification set. DCA and CIC showed that our model had a good net clinical benefit.ConclusionWe developed a forecast nomogram for delirium in the critically ill elderly patients that enhances clinical decision-making. However, further verification is required.