Model for predicting short-term mortality of severe sepsis.
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.
Adrie
Christophe
C
Medical-Surgical Intensive Care Unit, Delafontaine Hospital, 93205 Saint Denis, France. christophe.adrie@outcomerea.org
Francais
Adrien
A
Alvarez-Gonzalez
Antonio
A
Mounier
Roman
R
Azoulay
Elie
E
Zahar
Jean-Ralph
JR
Clec'h
Christophe
C
Goldgran-Toledano
Dany
D
Hammer
Laure
L
Descorps-Declere
Adrien
A
Jamali
Samir
S
Timsit
Jean-Francois
JF
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*