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Development of a mortality prediction nomogram for dementia patients using the MIMIC-IV database
Qi Deng1, Rong He1, Jianli Bai2
1Neurology Department, PKUCare Rehabilitation Hospital, Beijing, 102200, China.
Scientific Reports
|May 19, 2026
Summary
A new nomogram tool predicts mortality risk for dementia patients in Intensive Care Units (ICUs). This model aids clinical decisions by assessing individual risks, improving outcomes for this vulnerable population.
Area of Science:
- Gerontology and Public Health
- Critical Care Medicine
- Biostatistics and Predictive Modeling
Background:
- Global population aging presents dementia as a significant public health concern.
- Dementia patients in Intensive Care Units (ICUs) have high mortality rates.
- A lack of effective predictive models for ICU dementia patient mortality exists.
Purpose of the Study:
- To develop a nomogram-based tool for predicting mortality in ICU dementia patients.
- To identify independent predictors of all-cause mortality in this patient group.
Main Methods:
- A cohort of 2,280 dementia patients was randomly assigned to training and validation sets.
- Cox regression analysis identified mortality predictors, forming a nomogram.
- Model performance was assessed using Area Under the Curve (AUC), calibration curves, and Decision Curve Analysis (DCA).
Main Results:
- Independent predictors of mortality included age, race, blood glucose, Oxford Acute Severity of Illness Score (OASIS), antibiotic use, antihypertensive medication, and nephrotoxic drugs.
- The nomogram showed moderate predictive ability (AUCs ranging from 0.684 to 0.744) in both training and validation cohorts.
- Calibration curves and DCA confirmed good agreement and clinical utility.
Conclusions:
- A validated nomogram tool can estimate mortality risk for ICU dementia patients.
- The model offers individualized risk assessment and aids clinical decision-making.
- Challenges in cross-database generalizability were noted, highlighting the need for further research.
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