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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.
A retrospective, cross-sectional study.
Tertiary care academic hospital.
All adult inpatients who had a respiratory tract infection due to P. aeruginosa between January 2001 and June 2005.
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.
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.
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.
To identify institution-specific risk factors for MRSA bacteremia and develop an objective mechanism to estimate the probability of methicillin resistance in a given patient with Staphylococcus aureus bacteremia (SAB).
A cohort study was performed to identify institution-specific risk factors for MRSA. Logistic regression was used to model the likelihood of MRSA A stepwise approach was employed to derive a parsimonious model. The MRSA prediction tool was developed from the final model.
A 279-bed, level 1 trauma center.
Between January 1, 1999, and June 30, 2001, 494 patients with clinically significant episodes of SAB were identified.
The MRSA rate was 45.5%. Of 18 characteristics included in the logistic regression, the only independent features for MRSA were prior antibiotic exposure (OR, 9.2; CI95, 4.8 to 17.9), hospital onset (OR, 3.0; CI95, 1.9 to 4.9), history of hospitalization (OR, 2.5; CI95, 1.5 to 3.8), and presence of decubitus ulcers (OR, 2.5; CI95, 1.2 to 4.9). The prediction tool was derived from the final model, which was shown to accurately reflect the actual MRSA distribution in the cohort.
Through multivariate modeling techniques, we were able to identify the most important determinants of MRSA at our institution and develop a tool to predict the probability of methicillin resistance in a patient with SAB. This knowledge can be used to guide empiric antibiotic selection. In the era of antibiotic resistance, such tools are essential to prevent indiscriminate antibiotic use and preserve the longevity of current antimicrobials.
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