Delirium risk prediction models for intensive care unit patients: A systematic review. (October 2020)

• Record Type: Journal Article
• Authors: Chen, Junshan; Yu, Jintian; Zhang, Aiqin
• Is Part Of: Intensive and critical care nursing. Volume 60(2020)
• Publication Date: 2020-10
• Subjects: Delirium -- Prediction model -- ICU -- Systematic review; Intensive care nursing -- Periodicals; Critical Illness -- nursing -- Periodicals; Intensive Care -- Periodicals; Nursing Care -- Periodicals; Intensive care nursing; Periodicals; Electronic journals; 616.028
• DOI: 10.1016/j.iccn.2020.102880
• Languages: English
• ISSNs: 0964-3397

Abstract

Abstract: Objective: To systematically review the delirium risk prediction models for intensive care unit (ICU) patients. Methods: A systematic review was conducted. The Cochrane Library, PubMed, Ovid and Web of Science were searched to collect studies on delirium risk prediction models for ICU patients from database establishment to 31 March 2019. Two reviewers independently screened the literature according to the pre-determined inclusion and exclusion criteria, extracted the data and evaluated the risk of bias of the included studies using the CHARMS (CH ecklist for critical A ppraisal and data extraction for systematic R eviews of prediction M odelling S tudies) checklist. A descriptive analysis was used to describe and summarise the data. Results: A total of six models were included. All studies reported the area under the receiver operating characteristic curve (AUROC) of the prediction models in the derivation and (or) validation datasets as over 0.7 (from 0.75 to 0.9). Five models reported calibration metrics. Decreased cognitive reserve and the Acute Physiology and Chronic Health Evaluation II (APACHE-II) score were the most commonly reported predisposing and precipitating factors, respectively, of ICU delirium among all models. The small sample size, lack of external validation and the absence of or unreported blinding method increased the risk of bias. Conclusion: According to the discrimination and calibration statistics reported in the original studies, six prediction models may have moderate power in predicting ICU delirium. However, this finding should be interpreted with caution due to the risk of bias in the included studies. More clinical studies should be carried out to validate whether these tools have satisfactory predictive performance in delirium risk prediction for ICU patients.