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BACKGROUND: Failure to promptly identify deterioration in hospitalised patients is associated with delayed admission to intensive care units (ICUs) and poor outcomes. Existing vital sign-based Early Warning Score (EWS) algorithms do not have a sufficiently high positive predictive value to be used for automated activation of an ICU outreach team. Incorporating additional patient data might improve the predictive power of EWS algorithms; however, it is currently not known which patient data (or variables) are most predictive of ICU admission. We describe the protocol for a systematic review of variables associated with ICU admission. METHODS/DESIGN: MEDLINE, EMBASE, CINAHL and the Cochrane Library, including Cochrane Database of Systematic Reviews and the Cochrane Central Register of Controlled Trials (CENTRAL) will be searched for studies that assess the association of routinely recorded variables associated with subsequent unplanned ICU admission. Only studies involving adult patients admitted to general ICUs will be included. We will extract data relating to the statistical association between ICU admission and predictor variables, the quality of the studies and the generalisability of the findings. DISCUSSION: The results of this review will aid the development of future models which predict the risk of unplanned ICU admission. SYSTEMATIC REVIEW REGISTRATION: PROSPERO: CRD42015029617.

Original publication




Journal article


Syst Rev




Adult, Algorithms, Humans, Intensive Care Units, Patient Admission, Research Design, Review Literature as Topic, Risk Assessment, Risk Factors