Clinical Prediction Tool To Identify Patients withPseudomonas aeruginosaRespiratory Tract Infections at Greatest Risk for Multidrug Resistance

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Abstract
Despite the increasing prevalence of multiple-drug-resistant (MDR)Pseudomonas aeruginosa, the factors predictive of MDR have not been extensively explored. We sought to examine factors predictive of MDR among patients withP. aeruginosarespiratory tract infections and to develop a tool to estimate the probability of MDR among such high-risk patients. This was a single-site, case-control study of patients withP. aeruginosarespiratory tract infections. Multiple-drug resistance was defined as resistance to four or more antipseudomonal antimicrobial classes. Clinical data on demographics, antibiotic history, and microbiology were collected. Classification and regression tree analysis (CART) was used to identify the duration of antibiotic exposure associated with MDRP. aeruginosa. Log-binomial regression was used to model the probability of MDRP. aeruginosa. Among 351P. aeruginosa-infected patients, the proportion of MDRP. aeruginosawas 35%. A significant relationship between prior antibiotic exposure and MDRP. aeruginosawas found for all of the antipseudomonal antibiotics studied, but the duration of prior exposure associated with MDR varied between antibiotic classes; the shortest prior exposure duration was observed for carbapenems and fluoroquinolones, and the longest duration was noted for cefepime and piperacillin-tazobactam. Within the final model, the predicted MDRP. aeruginosalikelihood was most dependent upon length of hospital stay, prior culture sample collection, and number of CART-derived prior antibiotic exposures. A history of a prolonged hospital stay and exposure to antipseudomonal antibiotics predicts multidrug resistance among patients withP. aeruginosarespiratory tract infections at our institution. Identifying these risk factors enabled us to develop a prediction tool to assess the risk of resistance and thus guide empirical antibiotic therapy.