Development of a mortality prediction nomogram for dementia patients using the MIMIC-IV database

The accelerating global population aging underscores dementia as a critical public health challenge. Dementia patients admitted to Intensive Care Units (ICUs) face significantly elevated mortality risks, yet robust predictive models remain scarce. This study aimed to develop a nomogram-based tool for mortality prediction in ICU dementia patients. 2,280 dementia patients were randomly allocated to training (n = 1,596) and validation (n = 684) sets. Cox regression identified mortality predictors, incorporated into a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and Decision curve analysis (DCA). Age, race, blood glucose, Oxford Acute Severity of Illness Score (OASIS), antibiotic use, antihypertensive medication, and nephrotoxic drugs were independent predictors of all-cause mortality. The Cox model demonstrated AUCs for predicting 30-day and 90-day mortality in the training cohort were 0.727 and 0.744, respectively. In the validation cohort, corresponding AUCs were 0.684 and 0.706. Calibration curves indicated good agreement between predicted and actual mortality. DCA demonstrated the model’s clinical utility and net benefit. In conclusion, this study developed and validated a nomogram-based tool to estimate mortality risk in ICU patients with dementia. The model demonstrated moderate discriminative ability and clinical utility for risk stratification within the primary study cohort, while also underscoring the challenges of cross-database generalizability. By integrating multiple clinically relevant predictors, the model provides precise individualized risk assessments and offers a novel methodological framework for prognostic evaluation. The predictive capability of the model suggests its potential to facilitate clinical decision-making and improve patient outcomes.