Clinical Prediction Tool To Identify Patients with Pseudomonas aeruginosa Respiratory Tract Infections at Greatest Risk for Multidrug Resistance

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 with P. aeruginosa respiratory 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 with P. aeruginosa respiratory 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 MDR P. aeruginosa. Log-binomial regression was used to model the probability of MDR P. aeruginosa. Among 351 P. aeruginosa-infected patients, the proportion of MDR P. aeruginosa was 35%. A significant relationship between prior antibiotic exposure and MDR P. aeruginosa was 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 MDR P. aeruginosa likelihood 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 with P. aeruginosa respiratory 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.

Risk Factors for Multidrug-resistant Pseudomonas aeruginosa Among Hospitalized Patients at a Malaysian Hospital

“…Last but not least, despite the limitations, the analysis in this current study offers a preliminary picture on potential association to call for a large scale-study, particularly the association of antibiotic exposure and duration of hospital stay which have been shown to be significant in this study. Other similar associations have also been reported by many studies regardless of the socio-economic factors of the studied sample (Aloush et al 2006;Hsu et al 2005;Lodise et al 2007;Shanthi & Sekar 2009). These can be undoubtedly understood as antibiotic exposure will cause selective pressure for MDRPA to proliferate while hospital stay will increase the likelihood of acquiring the nosocomial spread.…”

“…The risk factors that are included in this study have also been investigated by many other researchers but whether those were significantly associated with the isolation of MDRPA had been differentially reported (Aloush et al 2006;Bahmani & Ramazanzadeh 2013;Gibu et al 2010;Hsu et al 2005;Lodise et al 2007;Shanthi & Sekar 2009). These are due to the variability in the socio-economic and health status of the studied population, as well as sample size in the respective study's setting.…”

Alterations in Two-Component Regulatory Systems of phoPQ and pmrAB Are Associated with Polymyxin B Resistance in Clinical Isolates of Pseudomonas aeruginosa

“…(3,12,18,19). P. aeruginosa accounts for 11 to 14% of all nosocomial infections and is a major problem for people hospitalized with cancer, cystic fibrosis, or burns (3).…”

Outbreaks, persistence, and high mortality rates of multiresistant Pseudomonas aeruginosa infections in a hospital with AIDS-predominant admissions

“…Inappropriate empirical antimicrobial-based therapy and delays to initiate appropriate antimicrobial-based therapy are both detrimental to patient outcome. 34 Authors have described failure of cefepime-susceptibility breakpoints to predict clinical outcomes 35 as well as the requirement of achieving adequate drug exposure to be successful in treating patients with P. aeruginosa infections. 36 Additionally, a multivariate logistic regression analysis demonstrated P. aeruginosa infection to be independently associated with treatment failure in cefepime-patients.…”