Pooled post-stroke delirium prediction models reached an AUC of 0.83, with age, stroke severity, and infection as common predictors
AI-summarized from the linked source. Educational brief, not medical advice.
Brief summary
A systematic review and meta-analysis of prediction models for post-stroke delirium found moderate-to-good pooled discrimination (AUC 0.83) and identified age, stroke severity, neutrophil-to-lymphocyte ratio, visual impairment, and infection as common predictors, while rating all included studies at high risk of bias.
What NurseJet pulled from the source
This systematic review searched nine databases through April 2026 and included 16 studies describing 24 prediction models for post-stroke delirium, with sample sizes from 100 to 14,475. Model discrimination was moderate to good (reported AUCs 0.72 to 0.94), and the meta-analytic pooled AUC was 0.83 (95% CI 0.81 to 0.85). Age, NIHSS score, neutrophil-to-lymphocyte ratio, visual impairment, and infection were the most common significant predictors. PROBAST assessment found a high overall risk of bias in all studies, driven mainly by shortcomings in the analysis domain; calibration was assessed in only six studies (with acceptable performance) and clinical utility was rarely evaluated. The authors conclude that existing models discriminate reasonably well on average but that their reliability in any single clinical setting remains uncertain.
Why this matters for nurses
Delirium after stroke is common and is linked to worse recovery, yet it is easy to miss on a busy neuro unit. This review may matter for nurses because it names the patient features most consistently tied to post-stroke delirium, giving a sense of who to watch most closely, while cautioning that current prediction models are not yet reliable enough to use on their own.
Bedside takeaway
Worth knowing that pooled post-stroke delirium models reached an AUC of 0.83, with age, stroke severity, neutrophil-to-lymphocyte ratio, visual impairment, and infection as the most common predictors.
Key takeaways
- Across 16 studies and 24 models, pooled discrimination for post-stroke delirium was an AUC of 0.83 (95% CI 0.81 to 0.85).
- The most common significant predictors were age, NIHSS score, neutrophil-to-lymphocyte ratio, visual impairment, and infection.
- Individual model AUCs ranged from 0.72 to 0.94, with sample sizes from 100 to 14,475.
- PROBAST rated every study at high overall risk of bias, mainly from analysis-domain shortcomings, and clinical utility was rarely tested.
Practice implications
- For neuro nurses, the recurring predictors (older age, higher NIHSS or stroke severity, signs of infection, and visual impairment) are practical cues to screen more vigilantly for delirium with a validated tool such as CAM-ICU or 4AT and to reinforce non-pharmacologic prevention (reorientation, sleep, early mobility, sensory aids). Because all models carried a high risk of bias, treat any risk score as a prompt for closer assessment, not a substitute for it.
Limitations & cautions
- The review pooled model-development and validation studies of varying design, and PROBAST rated all 16 at high overall risk of bias, so the true performance of these models is uncertain and may vary markedly between settings. Diagnostic criteria for post-stroke delirium differed across studies, calibration was reported in only six, and clinical utility was seldom evaluated; the analysis synthesizes predictors, not proof that acting on them changes outcomes.
- AI-summarized from the linked source. Review the original article before applying to practice.
Citations
Exact source links
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Frontiers in Neurology (PubMed)
Frontiers in Neurology (PubMed). Prediction models for post-stroke delirium: a systematic review with an exploratory meta-analysis of predictors.
https://pubmed.ncbi.nlm.nih.gov/42394927/
Professional education only
