title: Can binary early warning scores perform as well as standard early warning scores for discriminating a patient's risk of cardiac arrest, death or unanticipated intensive care unit admission?
abstract: BACKGROUND
  Although the weightings to be summed in an early warning score (EWS) calculation are small, calculation and other errors occur frequently, potentially impacting on hospital efficiency and patient care. Use of a simpler EWS has the potential to reduce errors.
  
  
  METHODS
  We truncated 36 published 'standard' EWSs so that, for each component, only two scores were possible: 0 when the standard EWS scored 0 and 1 when the standard EWS scored greater than 0. Using 1564,153 vital signs observation sets from 68,576 patient care episodes, we compared the discrimination (measured using the area under the receiver operator characteristic curve--AUROC) of each standard EWS and its truncated 'binary' equivalent.
  
  
  RESULTS
  The binary EWSs had lower AUROCs than the standard EWSs in most cases, although for some the difference was not significant. One system, the binary form of the National Early Warning System (NEWS), had significantly better discrimination than all standard EWSs, except for NEWS. Overall, Binary NEWS at a trigger value of 3 would detect as many adverse outcomes as are detected by NEWS using a trigger of 5, but would require a 15% higher triggering rate.
  
  
  CONCLUSIONS
  The performance of Binary NEWS is only exceeded by that of standard NEWS. It may be that Binary NEWS, as a simplified system, can be used with fewer errors. However, its introduction could lead to significant increases in workload for ward and rapid response team staff. The balance between fewer errors and a potentially greater workload needs further investigation.
authors.affiliation: Centre for Healthcare Modelling & Informatics, University of Portsmouth, Portsmouth, UK; Department of Health Sciences, University of York, York, UK.
authors.forename: Stuart
authors.initials: S
authors.lastname: Jarvis
authors.affiliation: Centre for Healthcare Modelling & Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: Caroline
authors.initials: C
authors.lastname: Kovacs
authors.affiliation: Centre for Healthcare Modelling & Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: Jim
authors.initials: J
authors.lastname: Briggs
authors.affiliation: Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Meredith
authors.affiliation: Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: Paul E
authors.initials: PE
authors.lastname: Schmidt
authors.affiliation: Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: Peter I
authors.initials: PI
authors.lastname: Featherstone
authors.affiliation: Centre for Healthcare Modelling & Informatics, University of Portsmouth, Portsmouth, UK; Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: David R
authors.initials: DR
authors.lastname: Prytherch
authors.affiliation: Faculty of Health and Social Sciences, University of Bournemouth, Bournemouth, UK. Electronic address: gbsresearch@virginmedia.com.
authors.forename: Gary B
authors.initials: GB
authors.lastname: Smith
mesh_terms: D003951:Diagnostic Errors / Q000517:prevention & control*; D061345:Early Medical Intervention / Q000379:methods / Q000592:standards; D004739:England / Q000453:epidemiology; D005260:Female; D000067495:Healthcare Failure Mode and Effect Analysis / Q000379:methods / Q000592:standards; D006323:Heart Arrest / Q000175:diagnosis / Q000401:mortality / Q000517:prevention & control; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000379:methods*; D010043:Outcome and Process Assessment, Health Care; D057216:Propensity Score; D012372:ROC Curve; D012720:Severity of Illness Index; D055986:Vital Signs
keywords: Failure to rescue; Hospital rapid response team; Monitoring; Physiologic; Vital signs