Risk prediction models for delirium in ICU patients: A systematic review and critical appraisal

This systematic review critically appraises 26 studies on risk prediction models for delirium in ICU patients. Despite the development of 25 distinct models incorporating common predictors like age, sedation use, and APACHE-II scores, and demonstrating apparently strong discriminatory performance, most models exhibited significant methodological limitations. These included widespread overfitting, inadequate handling of missing data, predominant reliance on internal validation only, and heterogeneous outcome assessment. Only four models underwent robust external validation. The findings indicate that while machine learning approaches like XGBoost show promise, fundamental methodological shortcomings substantially limit the clinical applicability and generalizability of existing prediction tools. Future research must prioritize methodological rigor, external validation in diverse populations, and implementation studies to assess real-world clinical impact before these models can be recommended for routine use.