title: Index blood tests and national early warning scores within 24 hours of emergency admission can predict the risk of in-hospital mortality: a model development and validation study.
abstract: BACKGROUND
  We explored the use of routine blood tests and national early warning scores (NEWS) reported within ±24 hours of admission to predict in-hospital mortality in emergency admissions, using empirical decision Tree models because they are intuitive and may ultimately be used to support clinical decision making.
  
  
  METHODS
  A retrospective analysis of adult emergency admissions to a large acute hospital during April 2009 to March 2010 in the West Midlands, England, with a full set of index blood tests results (albumin, creatinine, haemoglobin, potassium, sodium, urea, white cell count and an index NEWS undertaken within ±24 hours of admission). We developed a Tree model by randomly splitting the admissions into a training (50%) and validation dataset (50%) and assessed its accuracy using the concordance (c-) statistic. Emergency admissions (about 30%) did not have a full set of index blood tests and/or NEWS and so were not included in our analysis.
  
  
  RESULTS
  There were 23248 emergency admissions with a full set of blood tests and NEWS with an in-hospital mortality of 5.69%. The Tree model identified age, NEWS, albumin, sodium, white cell count and urea as significant (p<0.001) predictors of death, which described 17 homogeneous subgroups of admissions with mortality ranging from 0.2% to 60%. The c-statistic for the training model was 0.864 (95%CI 0.852 to 0.87) and when applied to the testing data set this was 0.853 (95%CI 0.840 to 0.866).
  
  
  CONCLUSIONS
  An easy to interpret validated risk adjustment Tree model using blood test and NEWS taken within ±24 hours of admission provides good discrimination and offers a novel approach to risk adjustment which may potentially support clinical decision making. Given the nature of the clinical data, the results are likely to be generalisable but further research is required to investigate this promising approach.
authors.affiliation: Primary Care Clinical Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom. m.a.mohammed@bham.ac.uk
authors.forename: Mohammed A
authors.initials: MA
authors.lastname: Mohammed
authors.forename: Gavin
authors.initials: G
authors.lastname: Rudge
authors.forename: Duncan
authors.initials: D
authors.lastname: Watson
authors.forename: Gordon
authors.initials: G
authors.lastname: Wood
authors.forename: Gary B
authors.initials: GB
authors.lastname: Smith
authors.forename: David R
authors.initials: DR
authors.lastname: Prytherch
authors.forename: Alan
authors.initials: A
authors.lastname: Girling
authors.forename: Andrew
authors.initials: A
authors.lastname: Stevens
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003657:Decision Making; D003663:Decision Trees; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D004739:England; D005260:Female; D006403:Hematologic Tests / Q000379:methods*; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010343:Patient Admission / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D012307:Risk Factors; D013997:Time Factors