The risk assessment tool for intensive care unit readmission: A systematic review and meta-analysis. (June 2023)

• Record Type: Journal Article
• Authors: Long, Jianying; Wang, Min; Li, Wenrui; Cheng, Jie; Yuan, Mengyuan; Zhong, Mingming; Zhang, Zhigang; Zhang, Caiyun
• Is Part Of: Intensive and critical care nursing. Volume 76(2023)
• Publication Date: 2023-06
• Subjects: Review -- Meta-analysis -- Critical care -- Readmission -- Risk assessment -- Prediction; 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.2022.103378
• Languages: English
• ISSNs: 0964-3397

Abstract

Abstract: Objective: To review and evaluate existing risk assessment tools for intensive care unitreadmission. Methods: Nine electronic databases (Medline, CINAHL, Web of Science, Cochrane Library, Embase, Sino Med, CNKI, VIP, and Wan fang) were systematically searched from their inception to September 2022. Two authors independently extracted data from the literature included. Meta-analysis was performed under the bivariate modeling and summary receiver operating characteristic curve method. Results: A total of 29 studies were included in this review, among which 11 were quantitatively Meta-analyzed. The results showed Stability and Workload Index for Transfer: Sensitivity = 0.55, Specificity = 0.65, Area under curve = 0.63. And Early warning score: Sensitivity = 0.78, Specificity = 0.83, Area under curve = 0.88. The remaining tools included scores, nomograms, machine learning models, and deep learning models. These studies, with varying reports on thresholds, case selection, data preprocessing, and model performance, have a high risk of bias. Conclusion: We cannot identify a tool that can be used directly in intensive care unit readmission risk assessment. Scores based on early warning score are moderately accurate in predicting readmission, but there is heterogeneity and publication bias that requires model adjustment for local factors such as resources, demographics, and case mix. Machine learning models present a promising modeling technique but have a high methodological bias and require further validation. Implications for clinical practice: Using reliable risk assessment tools is essential for the early identification of unplanned intensive care unit readmission risk in critically ill patients. A reliable risk assessment tool must be developed, which is the focus of further research.