title: A clinical prediction model to identify patients at high risk of death in the emergency department.
abstract: OBJECTIVE
  Rapid assessment and intervention is important for the prognosis of acutely ill patients admitted to the emergency department (ED). The aim of this study was to prospectively develop and validate a model predicting the risk of in-hospital death based on all available information available at the time of ED admission and to compare its discriminative performance with a non-systematic risk estimate by the triaging first health-care provider.
  
  
  METHODS
  Prospective cohort analysis based on a multivariable logistic regression for the probability of death.
  
  
  RESULTS
  A total of 8,607 consecutive admissions of 7,680 patients admitted to the ED of a tertiary care hospital were analysed. Most frequent APACHE II diagnostic categories at the time of admission were neurological (2,052, 24%), trauma (1,522, 18%), infection categories [1,328, 15%; including sepsis (357, 4.1%), severe sepsis (249, 2.9%), septic shock (27, 0.3%)], cardiovascular (1,022, 12%), gastrointestinal (848, 10%) and respiratory (449, 5%). The predictors of the final model were age, prolonged capillary refill time, blood pressure, mechanical ventilation, oxygen saturation index, Glasgow coma score and APACHE II diagnostic category. The model showed good discriminative ability, with an area under the receiver operating characteristic curve of 0.92 and good internal validity. The model performed significantly better than non-systematic triaging of the patient.
  
  
  CONCLUSIONS
  The use of the prediction model can facilitate the identification of ED patients with higher mortality risk. The model performs better than a non-systematic assessment and may facilitate more rapid identification and commencement of treatment of patients at risk of an unfavourable outcome.
authors: Clinical Trials Unit, Institute of Social and Preventive Medicine, University of Bern, 3010, Bern, Switzerland.
authors: Michael
authors: M
authors: Coslovsky
authors: Jukka
authors: J
authors: Takala
authors: Aristomenis K
authors: AK
authors: Exadaktylos
authors: Luca
authors: L
authors: Martinolli
authors: Tobias M
authors: TM
authors: Merz
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016638:Critical Illness / Q000401:mortality*; D003661:Decision Support Techniques; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012306:Risk; D014218:Triage / Q000379:methods; D055986:Vital Signs / Q000502:physiology; D055815:Young Adult