Identification of patients with Pseudomonas aeruginosa respiratory tract infections at greatest risk of infection with carbapenem-resistant isolates.

OBJECTIVE: To create a clinical tool based on institution-specific risk factors to estimate the probability of carbapenem resistance among Pseudomonas aeruginosa isolates obtained from infected patients. By better estimating the probability of carbapenem resistance on the basis of patient-specific factors, clinicians can refine their empirical therapy for P. aeruginosa infections and potentially maximize clinical outcomes by increasing the likelihood of appropriate empirical antimicrobial therapy. DESIGN: A retrospective, cross-sectional study. SETTING: Tertiary care academic hospital. PATIENTS: All adult inpatients who had a respiratory tract infection due to P. aeruginosa between January 2001 and June 2005. INTERVENTION: Data on demographic characteristics, antibiotic history, and microbiology were collected. Log-binomial regression was employed to identify predictors of carbapenem resistance among P. aeruginosa isolates and to devise the clinical prediction tool. RESULTS: Among 351 patients with P. aeruginosa infection, 44% were infected with carbapenem-resistant P. aeruginosa strains. Independent predictors of carbapenem resistance were prior receipt of mechanical ventilation for 11 days or more, prior exposure to fluoroquinolones for 3 days or more, and prior exposure to carbapenems for 3 days or more. CONCLUSIONS: With carbapenem resistance rates among P. aeruginosa isolates on the rise at our institution, the challenge was to identify patients for whom carbapenems would remain an effective empirical agent, as well as the patients at greatest risk for infection with carbapenem-resistant strains. The clinical prediction tool accurately estimated carbapenem resistance among this risk-stratified cross-sectional study of patients with P. aeruginosa infection. This tool may be an effective way for clinicians to refine their selection of empirical antibiotic therapy and to maximize clinical outcomes by increasing the likelihood of appropriate antibiotic treatment.