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AIMS OF STUDY:To develop and validate a centile-based early warning score using manually-recorded data (mCEWS). To compare mCEWS performance with a centile-based early warning score derived from continuously-acquired data (from bedside monitors, cCEWS), and with other published early warning scores. MATERIALS AND METHODS:We used an unsupervised approach to investigate the statistical properties of vital signs in an in-hospital patient population and construct an early-warning score from a "development" dataset. We evaluated scoring systems on a separate "validation" dataset. We assessed the ability of scores to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission, or death, each within 24 h of a given vital-sign observation, using metrics including the area under the receiver-operating characteristic curve (AUC). RESULTS:The development dataset contained 301,644 vital sign observations from 12,153 admissions (median age (IQR): 63 (49-73); 49.2% females) March 2014-September 2015. The validation dataset contained 1,459,422 vital-sign observations from 53,395 admissions (median age (IQR): 68 (48-81), 51.4% females) October 2015-May 2017. The AUC (95% CI) for the mCEWS was 0.868 (0.864-0.872), comparable with the National EWS, 0.867 (0.863-0.871), and other recently proposed scores. The AUC for cCEWS was 0.808 (95% CI, 0.804-0.812). The improvement in performance in comparison to the continuous CEWS was mainly explained by respiratory rate threshold differences. CONCLUSIONS:Performance of an EWS is highly dependent on the database from which itis derived. Our unsupervised statistical approach provides a straightforward, reproducible method to enable the rapid development of candidate EWS systems.

Original publication




Journal article



Publication Date





55 - 60


Nuffield Department of Clinical Neurosciences, Oxford University Hospitals NHS Trust, OX3 9DU Oxford, UK.


Humans, Heart Arrest, Hospitalization, Severity of Illness Index, Hospital Mortality, Risk Assessment, Statistical Distributions, Retrospective Studies, Follow-Up Studies, ROC Curve, Databases, Factual, Aged, Aged, 80 and over, Middle Aged, Intensive Care Units, Female, Male, Vital Signs, United Kingdom