A review of early warning systems for prompt detection of patients at risk for clinical decline : Journal of Trauma and Acute Care Surgery

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A review of early warning systems for prompt detection of patients at risk for clinical decline

Kramer, Andrew A. PhD; Sebat, Frank MD; Lissauer, Matthew MD

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Journal of Trauma and Acute Care Surgery 87(1S):p S67-S73, July 2019. | DOI: 10.1097/TA.0000000000002197


Early Warning Scores (EWS) are a composite evaluation of a patient's basic physiology, changes of which are the first indicators of clinical decline and are used to prompt further patient assessment and when indicated intervention. These are sometimes referred to as “track and triggers systems” with tracking meant to denote periodic observation of physiology and trigger being a predetermined response criteria. This review article examines the most widely used EWS, with special attention paid to those used in military and trauma populations.

The earliest EWS is the Modified Early Earning Score (MEWS). In MEWS, points are allocated to vital signs based on their degree of abnormality, and summed to yield an aggregate score. A score above a threshold would elicit a clinical response such as a rapid response team. Modified Early Earning Score was subsequently followed up with the United Kingdom's National Early Warning Score, the electronic cardiac arrest triage score, and the 10 Signs of Vitality score, among others.

Severity of illness indicators have been in military and civilian trauma populations, such as the Revised Trauma Score, Injury Severity Score, and Trauma and Injury Severity. The sequential organ failure assessment score and its attenuated version quick sequential organ failure assessment were developed to aggressively identify patients near septic shock.

Effective EWS have certain characteristics. First, they should accurately capture vital signs information. Second, almost all data should be derived electronically rather than manually. Third, the measurements should take into consideration multiple organ systems. Finally, information that goes into an EWS must be captured in a timely manner. Future trends include the use of machine learning to detect subtle changes in physiology and the inclusion of data from biomarkers. As EWS improve, they will be more broadly used in both military and civilian environments.


Review article, level I.

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