Prediction models for post-stroke delirium: a systematic review with an exploratory meta-analysis of predictors

 

Why are your predicting failure to recover RATHER THAN DELIVERING RECOVERY?

Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem!

You're all fired! You need to create EXACT RECOVERY PROTOCOLS! 

You've known of the need for years and delivered nothing!  Take a hike!

39% post stroke delirium (4 posts to July 2021)

Prediction crapola like this does nothing to get survivors recovered! Your comeuppance when you have a stroke and don't recover will be a bitter pill for you to swallow.

Prediction models for post-stroke delirium: a systematic review with an exploratory meta-analysis of predictors

• W

  Weiya Ma

• 

  Sumin Ma † *

• Q

  Qiaomin Tang † *

• Y

  Yuanyuan Sun

• C

  Chen Hu

• Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China

Abstract

Objective: 

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.

Methods: 

A comprehensive systematic search was conducted in nine databases from inception to April 2026. Studies developing or validating prediction models for PSD were included. Data extraction was guided by the CHARMS checklist. Methodological quality and risk of bias were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed to pool the effect sizes of predictors and the area under the receiver operating characteristic curve (AUC).

Results: 

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 ranged from 0.72 to 0.94. The meta-analytic pooled AUC was 0.83 (95% Confidence interval: 0.81–0.85). Age, NIHSS (National Institutes of Health Stroke Scale score), neutrophil-to-lymphocyte ratio, visual impairment, and infection were identified as common significant predictors. PROBAST assessment revealed a high overall risk of bias in all studies, primarily due to methodological shortcomings in the analysis domain. Calibration was assessed in six studies with acceptable performance, whereas clinical utility was rarely evaluated.

Conclusion: 

This study highlights several important predictors of PSD. However, due to the high risk of bias, the reliability of existing models remains uncertain. Although the pooled AUC of 0.84 suggests moderate to good discrimination, its performance in individual clinical settings may vary markedly. 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.

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