To systematically identify and synthesize predictors of post-stroke delirium (PSD) derived from existing prediction models, and to assess the methodological quality of these studies using PROBAST.
Approach:
Key Findings:
Sixteen studies (24 models) with sample sizes ranging from 100 to 14,475 were included.
Model discrimination was moderate to good, with reported AUC values ranging from 0.72 to 0.94.
The meta-analytic pooled AUC was 0.83 (95% Confidence interval: 0.81–0.85).
Common significant predictors identified include age, NIHSS score, neutrophil-to-lymphocyte ratio, visual impairment, and infection.
PROBAST assessment revealed a high overall risk of bias in all studies, primarily due to methodological shortcomings.
Interpretation:
Although the pooled AUC of 0.84 suggests moderate to good discrimination, its performance in individual clinical settings may vary markedly.
Limitations:
High risk of bias in all studies due to methodological shortcomings.
Calibration was assessed in only six studies with acceptable performance.
Clinical utility was rarely evaluated.
Conclusion:
Future studies should adhere to unified PSD diagnosis criteria, employ robust validation strategies, and explore advanced modeling techniques to improve model reliability and clinical utility.
