2363. Identification of Risk Factors to Predict Pseudomonas aeruginosa and Methicillin-Resistant Staphylococcus aureus in Patients With Infected Chronic Foot Ulcers

BACKGROUND: Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA) have traditionally been considered prevalent pathogens in foot infections. Whether empiric therapy directed against these organisms is necessary, and in which specific patient population, remains unclear. The aim of this study was to identify risk factors to forecast the probability of isolating P. aeruginosa or MRSA in these infected wounds. METHODS: We reviewed the records of 140 patients with infected chronic foot ulcers. Data on baseline demographic, clinical, surgical, microbiology, and treatment parameters were collected. Multivariable logistic regression models, validated via bootstrapping methods, were used to establish risk factors associated with isolation of these organisms. We then used these models to build predictive nomograms for clinical use, and to calculate sensitivity, specificity, positive and negative predictive values. RESULTS: A total of 307 bacterial isolates were identified, most frequently MRSA (24.3%). P. aeruginosa was found in 14.3% of these cultures. Amputation (OR 5.75, 95% CI 1.48–27.63) and renal disease (OR 5.46, 95% CI 1.43–25.16) were associated with higher P. aeruginosa isolation, whereas, diabetes (OR 0.07, 95% CI 0.01–0.34) and IDSA infection category >3 (OR 0.18, 95% CI 0.03–0.65) were associated with lower odds (Figure 1). Analysis for MRSA showed that amputation was associated with lower (OR 0.29, 95% CI 0.09–0.79) risk, while history of MRSA infection (OR 5.63, 95% CI 1.56–20.63) was associated with higher odds of isolating this organism (Figure 2). The models’ ability to discriminate was found to be reasonable to strong, as evidenced by the optimism-corrected C statistic of 0.81 and 0.69, respectively. CONCLUSION: We developed easy to use nomograms based on logistic regression models with strong predictive performances to forecast risk of drug-resistant pathogens. They may be used in clinical practice to judge the probability of isolating these two resistance prone organisms. [Image: see text] DISCLOSURES: All authors: No reported disclosures.