title: Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS).
abstract: OBJECTIVE
  To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis.
  
  
  METHODS
  We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve.
  
  
  RESULTS
  The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome].
  
  
  CONCLUSIONS
  The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: Tessy
authors.initials: T
authors.lastname: Badriyah
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: James S
authors.initials: JS
authors.lastname: Briggs
authors.affiliation: Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Meredith
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: Stuart W
authors.initials: SW
authors.lastname: Jarvis
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK; Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: Paul E
authors.initials: PE
authors.lastname: Schmidt
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK; Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: Peter I
authors.initials: PI
authors.lastname: Featherstone
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK; Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: David R
authors.initials: DR
authors.lastname: Prytherch
authors.affiliation: School of Health and Social Care, University of Bournemouth, Bournemouth, UK. Electronic address: gbsresearch@virginmedia.com.
authors.forename: Gary B
authors.initials: GB
authors.lastname: Smith
mesh_terms: D000368:Aged; D003663:Decision Trees; D005260:Female; D006323:Heart Arrest / Q000175:diagnosis*; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008991:Monitoring, Physiologic; D012720:Severity of Illness Index
keywords: Early warning scores; Medical emergency team; Outreach; Risk prediction; Scoring systems; Track and trigger systems