Systematic review of risk prediction models for unplanned ICU readmission
Wang Ting, Yang Yang, Liu Ning
Published 2022-10-06
Cite as Chin J Mod Nurs, 2022, 28(28): 3913-3918. DOI: 10.3760/cma.j.cn115682-20220215-00683
Abstract
ObjectiveTo systematically evaluate the predictive value of the risk prediction models for unplanned ICU readmission.
MethodsThe Cochrane Library, PubMed, Embase, Wanfang database, VIP Network, China National Knowledge Infrastructure and other Chinese and English databases were systematically searched to collect studies on risk prediction models of unplanned ICU readmission. The search time was from the establishment of the databases to January 1, 2022. Two researchers independently screened the literature and extracted the data to evaluate the risk of bias in the included studies and the accuracy of the different models.
ResultsFinally, 15 articles were included, the proportion of unplanned ICU readmission was 2.54%-13.13% and the area under the receiver operating characteristic curve of each prediction model was 0.660-0.858. Among the 15 included literatures, only 5 prediction models were externally validated and most models had some risk of bias in the modeling process, but their overall applicability was good.
ConclusionsMost of the prediction models of unplanned ICU readmission have some methodological and statistical defects. Medical staff should be careful to explain the prediction effect in clinical application and future research should focus on the external validation of the models.
Key words:
Intensive Care Units; Unplanned ICU readmission; Prediction model; System review
Contributor Information
Wang Ting
School of Nursing, Zhuhai Campus of Zunyi Medical University, Zhuhai 519040, China
Yang Yang
School of Nursing, Zhuhai Campus of Zunyi Medical University, Zhuhai 519040, China
Liu Ning
Department of Nursing, Teaching and Research Office, Zhuhai Campus of Zunyi Medical University, Zhuhai 519040, China
