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ICU patients have a 37.158% prevalence of subsyndromal delirium; vasoactive drug use predicts risk.

ICU patients have a 37.158% prevalence of subsyndromal delirium; vasoactive drug use predicts risk.
Photo by Olga Kononenko / Unsplash
Key Takeaway
Note that vasoactive drug use is a significant predictive factor for subsyndromal delirium in ICU patients.

This prospective cohort study assessed the incidence and influencing factors of subsyndromal delirium within an ICU setting. The sample size was not reported, and the specific intervention or comparator was not reported in the provided data. The primary outcome measured the prevalence of subsyndromal delirium, which was found to be 37.158% among the studied population.

A predictive model utilizing the XGB algorithm demonstrated the best performance with an AUC of 0.84. Feature importance analysis identified several significant predictive factors, including the use of vasoactive drugs (effect size 0.412), monthly household income (effect size 0.306), having undergone surgery (effect size 0.191), and the number of medications (effect size 0.036). No absolute numbers or p-values were reported for these outcomes.

Safety and tolerability data were not reported, and adverse events were not documented. The study limitations note that future multicenter studies should validate these results in larger cohorts. Given the observational nature of the study, causal relationships cannot be established. These findings may enable clinicians to stratify high-risk groups and implement timely and targeted intervention measures, effectively reducing the risk of adverse consequences, but practice relevance remains uncertain without further validation.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
BackgroundThis study aims to develop and validate a machine learning-based risk prediction model for subsyndromal delirium (SSD) in ICU patients, while identifying key risk factors.MethodThis study was a prospective study, selecting patients who were hospitalized in the ICU from October 2024 to May 2025. We compared seven machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Elastic Network (EN), Extreme Gradient Enhancement (XGB), and Support Vector Machine (SVM).ResultIn our study, the prevalence rate of SSD was 37.158%. The comparative analysis shows that XGB is the best predictive model (AUC = 0.84). Feature importance analysis identified four significant predictive factors: Use of vasoactive drugs (0.412), Monthly household income (0.306), Undergone surgery (0.191) and Number of Medications (0.036).ConclusionThe prediction model based on XGB has a good effect in identifying the risk of SSD in ICU patients. These findings enable clinicians to stratify high-risk groups and implement timely and targeted intervention measures, effectively reducing the risk of adverse consequences. Future multicenter studies should validate these results in larger cohorts.
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