Risk Prediction Models of Subsyndromal Delirium in Critically Ill Patients: A Systematic Review and Meta-Analysis.

Abstract

Background: The number of predictive models for assessing the risk of subsyndromal delirium (SSD) in critically ill patients is increasing, yet the quality and applicability of these models in clinical practice remain unclear.

Aim: To systematically review and critically evaluate the existing risk prediction models.

Study design: Eleven Chinese and English databases, including PubMed, Web of Science and Embase, were searched from their inception to August 16, 2024. Two researchers independently screened the literature, extracted data and assessed the risk of bias and applicability using the prediction model risk of bias assessment tool. Meta-analysis was conducted using Stata 17.0.

Results: Eight studies were included. The SSD incidence in ICU patients ranged from 8.97% to 34.5%. The most commonly used predictors were the APACHE II score and age. The reported area under the curve (AUC) ranged from 0.788 to 0.923, with the pooled AUC value for the five validated models being 0.87 (95% CI: 0.82-0.92). Six studies had a high risk of bias, while two had an unclear risk.

Conclusions: The eight included models demonstrated good performance in early identification and screening of high-risk critically ill patients for SSD, but they all exhibited a high risk of bias regarding model quality.

Relevance to clinical practice: ICU professionals should carefully select and validate existing models based on their specific clinical settings before applying them. Alternatively, they can conduct new models incorporating multimodal data and artificial intelligence algorithms, utilizing large sample sizes, robust research designs and multi-center external validation.