title: Hospital-acquired pneumonia: risk factors for antimicrobial-resistant causative pathogens in critically ill patients.
abstract: OBJECTIVE
  To determine factors associated with antimicrobial-resistant hospital-acquired pneumonia (AR-HAP), to build an algorithm evaluating the risk for such a pneumonia, and to test this algorithm.
  
  
  METHODS
  Combined observational and validation cohorts over two periods: January 1994 to December 1999, and January 2000 to March 2001.
  
  
  METHODS
  One ICU from a university-affiliated urban teaching hospital.
  
  
  METHODS
  One hundred twenty-four patients in the observational cohort and 26 patients in the validation cohort exhibiting bacteriologically documented hospital-acquired pneumonia (HAP).
  
  
  METHODS
  Prospective data collection and multivariate analysis using the chi(2) automatic interaction and detection technique.
  
  
  RESULTS
  In the observational cohort, 39 antimicrobial-resistant bacteria were incriminated in 37 patients (30%). Multivariate analysis identified four independent variables allowing a binary stratification of risk. According to the presence or absence of prior antimicrobial treatment, neurologic disturbances on ICU admission, aspiration on ICU admission, and time elapsed between ICU admission and the onset of pneumonia, we were able to identify and separate patients at high, low, or even no risk for acquiring AR-HAP. In the validation cohort, nine antimicrobial-resistant bacteria were incriminated in nine patients (34.6%). In this cohort, the algorithm performed well allowing the identification of null risk categories: the absence of prior antimicrobial treatment, the presence of prior antimicrobial treatment with neurologic disturbances on ICU admission and an early-onset pneumonia, and the presence of prior antimicrobial treatment without neurologic disturbances but with aspiration on ICU admission were always associated with antimicrobial-susceptible HAP.
  
  
  CONCLUSIONS
  We developed and tested a binary algorithm allowing the identification of patients at low risk for acquiring AR-HAP. An antibiotic strategy including an initial antimicrobial treatment guided by such an algorithm, followed, if possible, by a de-escalation when antimicrobial data are available, could increase the administration of adequate initial antimicrobial treatment and help prevent the emergence of antibiotic resistance in the ICU.
authors.affiliation: Service de Réanimation Médicale et Maladies Infectieuses, Université de Lille, Center Hospitalier, Tourcoing, France. oyleroy@caramail.com
authors.forename: Olivier
authors.initials: O
authors.lastname: Leroy
authors.forename: Sandrine
authors.initials: S
authors.lastname: Jaffré
authors.forename: Thibaud
authors.initials: T
authors.lastname: D'Escrivan
authors.forename: Patrick
authors.initials: P
authors.lastname: Devos
authors.forename: Hugues
authors.initials: H
authors.lastname: Georges
authors.forename: Serge
authors.initials: S
authors.lastname: Alfandari
authors.forename: Gilles
authors.initials: G
authors.lastname: Beaucaire
mesh_terms: D000465:Algorithms; D003428:Cross Infection / Q000382:microbiology*; D024881:Drug Resistance, Bacterial; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D018410:Pneumonia, Bacterial / Q000382:microbiology*; D012307:Risk Factors