title: Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions.
abstract: OBJECTIVE
  To build an early warning score (EWS) based exclusively on routinely undertaken laboratory tests that might provide early discrimination of in-hospital death and could be easily implemented on paper.
  
  
  METHODS
  Using a database of combined haematology and biochemistry results for 86,472 discharged adult patients for whom the admission specialty was Medicine, we used decision tree (DT) analysis to generate a laboratory decision tree early warning score (LDT-EWS) for each gender. LDT-EWS was developed for a single set (n=3496) (Q1) and validated in 22 other discrete sets each of three months long (Q2, Q3…Q23) (total n=82,976; range of n=3428 to 4093) by testing its ability to discriminate in-hospital death using the area under the receiver-operating characteristic (AUROC) curve.
  
  
  RESULTS
  The data generated slightly different models for male and female patients. The ranges of AUROC values (95% CI) for LDT-EWS with in-hospital death as the outcome for the validation sets Q2-Q23 were: 0.755 (0.727-0.783) (Q16) to 0.801 (0.776-0.826) [all patients combined, n=82,976]; 0.744 (0.704-0.784, Q16) to 0.824 (0.792-0.856, Q2) [39,591 males]; and 0.742 (0.707-0.777, Q10) to 0.826 (0.796-0.856, Q12) [43,385 females].
  
  
  CONCLUSIONS
  This study provides evidence that the results of commonly measured laboratory tests collected soon after hospital admission can be represented in a simple, paper-based EWS (LDT-EWS) to discriminate in-hospital mortality. We hypothesise that, with appropriate modification, it might be possible to extend the use of LDT-EWS throughout the patient's hospital stay.
authors.affiliation: Centre for Healthcare Modelling and Informatics, University of Portsmouth, Portsmouth, UK.
authors.forename: Stuart W
authors.initials: SW
authors.lastname: Jarvis
authors.forename: Caroline
authors.initials: C
authors.lastname: Kovacs
authors.forename: Tessy
authors.initials: T
authors.lastname: Badriyah
authors.forename: Jim
authors.initials: J
authors.lastname: Briggs
authors.forename: Mohammed A
authors.initials: MA
authors.lastname: Mohammed
authors.forename: Paul
authors.initials: P
authors.lastname: Meredith
authors.forename: Paul E
authors.initials: PE
authors.lastname: Schmidt
authors.forename: Peter I
authors.initials: PI
authors.lastname: Featherstone
authors.forename: David R
authors.initials: DR
authors.lastname: Prytherch
authors.forename: Gary B
authors.initials: GB
authors.lastname: Smith
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D003663:Decision Trees; D003955:Diagnostic Tests, Routine; D004630:Emergencies; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests
keywords: Biochemistry; Early warning scores; Haematology; Illness severity score; Risk prediction