title: Model for predicting short-term mortality of severe sepsis.
abstract: BACKGROUND
  To establish a prognostic model for predicting 14-day mortality in ICU patients with severe sepsis overall and according to place of infection acquisition and to sepsis episode number.
  
  
  METHODS
  In this prospective multicentre observational study on a multicentre database (OUTCOMEREA) including data from 12 ICUs, 2268 patients with 2737 episodes of severe sepsis were randomly divided into a training cohort (n = 1458) and a validation cohort (n = 810). Up to four consecutive severe sepsis episodes per patient occurring within the first 28 ICU days were included. We developed a prognostic model for predicting death within 14 days after each episode, based on patient data available at sepsis onset.
  
  
  RESULTS
  Independent predictors of death were logistic organ dysfunction (odds ratio (OR), 1.22 per point, P < 10-4), septic shock (OR, 1.40; P = 0.01), rank of severe sepsis episode (1 reference, 2: OR, 1.26; P = 0.10 >or= 3: OR, 2.64; P < 10-3), multiple sources of infection (OR; 1.45, P = 0.03), simplified acute physiology score II (OR, 1.02 per point; P < 10-4), McCabe score ([greater than or equal to]2) (OR, 1.96; P < 10-4), and number of chronic co-morbidities (1: OR, 1.75; P < 10-3, >or= 2: OR, 2.24, P < 10-3). Validity of the model was good in whole cohorts (AUC-ROC, 0.76; 95%CI, 0.74 to 0.79; and HL Chi-square: 15.3 (P = 0.06) for all episodes pooled).
  
  
  CONCLUSIONS
  In ICU patients, a prognostic model based on a few easily obtained variables is effective in predicting death within 14 days after the first to fourth episode of severe sepsis complicating community-, hospital-, or ICU-acquired infection.
authors.affiliation: Medical-Surgical Intensive Care Unit, Delafontaine Hospital, 93205 Saint Denis, France. christophe.adrie@outcomerea.org
authors.forename: Christophe
authors.initials: C
authors.lastname: Adrie
authors.forename: Adrien
authors.initials: A
authors.lastname: Francais
authors.forename: Antonio
authors.initials: A
authors.lastname: Alvarez-Gonzalez
authors.forename: Roman
authors.initials: R
authors.lastname: Mounier
authors.forename: Elie
authors.initials: E
authors.lastname: Azoulay
authors.forename: Jean-Ralph
authors.initials: JR
authors.lastname: Zahar
authors.forename: Christophe
authors.initials: C
authors.lastname: Clec'h
authors.forename: Dany
authors.initials: D
authors.lastname: Goldgran-Toledano
authors.forename: Laure
authors.initials: L
authors.lastname: Hammer
authors.forename: Adrien
authors.initials: A
authors.lastname: Descorps-Declere
authors.forename: Samir
authors.initials: S
authors.lastname: Jamali
authors.forename: Jean-Francois
authors.initials: JF
authors.lastname: Timsit
mesh_terms: D000368:Aged; D003661:Decision Support Techniques; D005260:Female; D005602:France / Q000453:epidemiology; D006305:Health Status Indicators; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011379:Prognosis; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D018805:Sepsis / Q000175:diagnosis* / Q000401:mortality*