title: Are observation selection methods important when comparing early warning score performance?
abstract: BACKGROUND
  Sicker patients generally have more vital sign assessments, particularly immediately before an adverse outcome, and especially if the vital sign monitoring schedule is driven by an early warning score (EWS) value. This lack of independence could influence the measured discriminatory performance of an EWS.
  
  
  METHODS
  We used a population of 1564,143 consecutive vital signs observation sets collected as a routine part of patients' care. We compared 35 published EWSs for their discrimination of the risk of death within 24h of an observation set using (1) all observations in our dataset, (2) one observation per patient care episode, chosen at random and (3) one observation per patient care episode, chosen as the closest to a randomly selected point in time in each episode. We compared the area under the ROC curve (AUROC) as a measure of discrimination for each of the 35 EWSs under each observation selection method and looked for changes in their rank order.
  
  
  RESULTS
  There were no significant changes in rank order of the EWSs based on AUROC between the different observation selection methods, except for one EWS that included age among its components. Whichever method of observation selection was used, the National Early Warning Score (NEWS) showed the highest discrimination of risk of death within 24h. AUROCs were higher when only one observation set was used per episode of care (significantly higher for many EWSs, including NEWS).
  
  
  CONCLUSIONS
  Vital sign measurements can be treated as if they are independent - multiple observations can be used from each episode of care--when comparing the performance and ranking of EWSs, provided no EWS includes age.
authors: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK; Department of Health Sciences, University of York, York, UK.
authors: Stuart W
authors: SW
authors: Jarvis
authors: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors: Caroline
authors: C
authors: Kovacs
authors: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors: Jim
authors: J
authors: Briggs
authors: TEAMS Centre, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors: Paul
authors: P
authors: Meredith
authors: Acute Medicine, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors: Paul E
authors: PE
authors: Schmidt
authors: Acute Medicine, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors: Peter I
authors: PI
authors: Featherstone
authors: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK; TEAMS Centre, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors: David R
authors: DR
authors: Prytherch
authors: School of Health & Social Care, University of Bournemouth, Bournemouth, UK. Electronic address: gbsresearch@virginmedia.com.
authors: Gary B
authors: GB
authors: Smith
mesh_terms: D000367:Age Factors; D016638:Critical Illness / Q000401:mortality*; D042241:Early Diagnosis; D006801:Humans; D008991:Monitoring, Physiologic; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012720:Severity of Illness Index; D055986:Vital Signs
keywords: Early warning score; Illness severity score; Monitoring; Risk; Vital signs