Manual centile-based early warning scores derived from statistical distributions of observational vital-sign data. (August 2018)

• Record Type: Journal Article
• Authors: Watkinson, Peter J.; Pimentel, Marco A.F.; Clifton, David A.; Tarassenko, Lionel
• Is Part Of: Resuscitation. Volume 129(2018)
• Page Start: 55
• Page End: 60
• Publication Date: 2018-08
• Subjects: Physiological monitoring -- Vital signs -- Early warning system -- Risk scoring systems -- Medical emergency team; Resuscitation -- Periodicals; Resuscitation -- Periodicals; Réanimation -- Périodiques; Electronic journals; 616.025
• DOI: 10.1016/j.resuscitation.2018.06.003
• Languages: English
• ISSNs: 0300-9572

Abstract

Abstract: 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.