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Ability of an antibiogram to predict Pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation.

Publication ,  Journal Article
Anderson, DJ; Miller, B; Marfatia, R; Drew, R
Published in: Infect Control Hosp Epidemiol
June 2012

OBJECTIVE: To determine the utility of an antibiogram in predicting the susceptibility of Pseudomonas aeruginosa isolates to targeted antimicrobial agents based on the day of hospitalization the specimen was collected. DESIGN: Single-center retrospective cohort study. SETTING: A 750-bed tertiary care medical center. PATIENTS AND METHODS: Isolates from consecutive patients with at least 1 clinical culture positive for P. aeruginosa from January 1, 2000, to June 30, 2007, were included. A study antibiogram was created by determining the overall percentages of P. aeruginosa isolates susceptible to amikacin, ceftazidime, ciprofloxacin, gentamicin, imipenem-cilastin, piperacillin-tazobactam, and tobramycin during the study period. Individual logistic regression models were created to determine the day of infection after which the study antibiogram no longer predicted susceptibility to each antibiotic. RESULTS: A total of 3,393 isolates were included. The antibiogram became unreliable as a predictor of susceptibility to ceftazidime, imipenem-cilastin, piperacillin-tazobactam, and tobramycin after day 10 and ciprofloxacin after day 15 but longer for gentamicin (day 21) and amikacin (day 28). Time to unreliability of the antibiogram varied for antibiotics based on location of isolation. For example, the time to unreliability of the antibiogram for ceftazidime was 5 days (95% confidence interval [CI], <1-8) in the intensive care unit (ICU) and 12 days (95% CI, 7-21) in non-ICU hospital wards (P = .003). CONCLUSIONS: The ability of the antibiogram to predict susceptibility of P. aeruginosa decreases as duration of hospitalization increases.

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Published In

Infect Control Hosp Epidemiol

DOI

EISSN

1559-6834

Publication Date

June 2012

Volume

33

Issue

6

Start / End Page

589 / 593

Location

United States

Related Subject Headings

  • Time Factors
  • Retrospective Studies
  • Reproducibility of Results
  • Pseudomonas aeruginosa
  • Pseudomonas Infections
  • Microbial Sensitivity Tests
  • Logistic Models
  • Length of Stay
  • Humans
  • Hospitalization
 

Citation

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Anderson, D. J., Miller, B., Marfatia, R., & Drew, R. (2012). Ability of an antibiogram to predict Pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation. Infect Control Hosp Epidemiol, 33(6), 589–593. https://doi.org/10.1086/665721
Anderson, Deverick J., Becky Miller, Ruchit Marfatia, and Richard Drew. “Ability of an antibiogram to predict Pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation.Infect Control Hosp Epidemiol 33, no. 6 (June 2012): 589–93. https://doi.org/10.1086/665721.
Anderson DJ, Miller B, Marfatia R, Drew R. Ability of an antibiogram to predict Pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation. Infect Control Hosp Epidemiol. 2012 Jun;33(6):589–93.
Anderson, Deverick J., et al. “Ability of an antibiogram to predict Pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation.Infect Control Hosp Epidemiol, vol. 33, no. 6, June 2012, pp. 589–93. Pubmed, doi:10.1086/665721.
Anderson DJ, Miller B, Marfatia R, Drew R. Ability of an antibiogram to predict Pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation. Infect Control Hosp Epidemiol. 2012 Jun;33(6):589–593.
Journal cover image

Published In

Infect Control Hosp Epidemiol

DOI

EISSN

1559-6834

Publication Date

June 2012

Volume

33

Issue

6

Start / End Page

589 / 593

Location

United States

Related Subject Headings

  • Time Factors
  • Retrospective Studies
  • Reproducibility of Results
  • Pseudomonas aeruginosa
  • Pseudomonas Infections
  • Microbial Sensitivity Tests
  • Logistic Models
  • Length of Stay
  • Humans
  • Hospitalization