title: Comparison of the National Early Warning Score in non-elective medical and surgical patients.
abstract: BACKGROUND
  The National Early Warning Score (NEWS) is used to identify deteriorating patients in hospital. NEWS is a better discriminator of outcomes than other early warning scores in acute medical admissions, but it has not been evaluated in a surgical population. The study aims were to evaluate the ability of NEWS to discriminate cardiac arrest, death and unanticipated ICU admission in patients admitted to surgical specialties, and to compare the performance of NEWS in admissions to medical and surgical specialties.
  
  
  METHODS
  Hospitalwide data over 31 months, from adult inpatients who stayed at least one night or died on the day of admission, were analysed. The data were categorized as elective or non-elective surgical or medical admissions. The ability of NEWS to discriminate the outcomes above in these different groups was assessed using the area under the receiver operating characteristic curve (AUROC).
  
  
  RESULTS
  There were too few outcomes to permit meaningful comparison of elective admissions, so the analysis was constrained to comparison of non-elective admissions. NEWS performed equally well, or better, for surgical as for medical patients. For death within 24 h the AUROC for surgical admissions was 0·914 (95 per cent c.i. 0·907 to 0·922), compared with 0·902 (0·898 to 0·905) for medical admissions. For the combined outcome of any of death, cardiac arrest or unanticipated ICU admission, the AUROC was 0·874 (0·868 to 0·880) for surgical admissions and 0·874 (0·871 to 0·877) for medical admissions.
  
  
  CONCLUSIONS
  NEWS discriminated deterioration in non-elective surgical patients at least as well as in non-elective medical patients.
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: C
authors.initials: C
authors.lastname: Kovacs
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.|Department of Health Sciences, University of York, York, UK.
authors.forename: S W
authors.initials: SW
authors.lastname: Jarvis
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.|Research and Innovation Department, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: D R
authors.initials: DR
authors.lastname: Prytherch
authors.affiliation: Research and Innovation Department, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: P
authors.initials: P
authors.lastname: Meredith
authors.affiliation: Department of Acute Medicine, Portsmouth Hospitals NHS Trust, Portsmouth, UK.
authors.forename: P E
authors.initials: PE
authors.lastname: Schmidt
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: J S
authors.initials: JS
authors.lastname: Briggs
authors.affiliation: Faculty of Health and Social Sciences, University of Bournemouth, Bournemouth, UK.
authors.forename: G B
authors.initials: GB
authors.lastname: Smith
mesh_terms: D000328:Adult; D019540:Area Under Curve; D004630:Emergencies; D006323:Heart Arrest / Q000175:diagnosis; D006748:Hospital Departments; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D010343:Patient Admission / Q000706:statistics & numerical data; D011379:Prognosis; D012372:ROC Curve; D018570:Risk Assessment; D012720:Severity of Illness Index; D013503:Surgery Department, Hospital; D006113:United Kingdom; D055986:Vital Signs