Clinical predictive model of multidrug resistance in neutropenic cancer patients with bloodstream infection due to Pseudomonas aeruginosa

We aimed to assess the rate and predictive factors of bloodstream infection (BSI) due to multidrug-resistant (MDR) Pseudomonas aeruginosa in neutropenic cancer patients. We performed a multicenter, retrospective cohort study including oncohematological neutropenic patients with BSI due to P. aeruginosa conducted across 34 centers in 12 countries from January 2006 to May 2018. A mixed logistic regression model was used to estimate a model to predict the multidrug resistance of the causative pathogens. of a total of 1,217 episodes of BSI due to P. aeruginosa, 309 episodes (25.4%) were caused by MDR strains. the rate of multidrug resistance increased significantly over the study period (P = 0.033). Predictors of MDR P. aeruginosa BSI were prior therapy with piperacillin-tazobactam (odds ratio [OR), 3.48; 95% confidence interval [CI], 2.29 to 5.30), prior antipseudomonal carbapenem use (OR, 2.53; 95% CI, 1.65 to 3.87), fluoroquinolone prophylaxis (OR, 2.99; 95% CI, 1.92 to 4.64), underlying hematological disease (OR, 2.09; 95% CI, 1.26 to 3.44), and the presence of a urinary catheter (OR, 2.54; 95% CI, 1.65 to 3.91), whereas older age (OR, 0.98; 95% CI, 0.97 to 0.99) was found to be protective. Our prediction model achieves good discrimination and calibration, thereby identifying neutropenic patients at higher risk of BSI due to MDR P. aeruginosa. the application of this model using a web-based calculator may be a simple strategy to identify high-risk patients who may benefit from the early administration of broad-spectrum antibiotic coverage against MDR strains according to the local susceptibility patterns, thus avoiding the use of broad-spectrum antibiotics in patients at a low risk of resistance development.
ESGBIES study group; ESGICH study group; Spanish Plan Nacional de I+D+i 2013-2016; Instituto de Salud Carlos III, Subdireccion General de Redes y Centros de Investigacion Cooperativa, Ministerio de Economia, Industria y Competitividad, Spanish Network for Research in Infectious Diseases [REIPI RD16/0016/0001]; European Development Regional Fund A Way To Achieve Europe, Operative Program Intelligent Growth 2014-2020; Promex Stiftung fur die Forschung (Carigest SA); GileadGilead Sciences; PfizerPfizer
We thank the ESGBIES and the ESGICH study groups for supporting the study.; This study was supported by the Spanish Plan Nacional de I+D+i 2013-2016 and the Instituto de Salud Carlos III, Subdireccion General de Redes y Centros de Investigacion Cooperativa, Ministerio de Economia, Industria y Competitividad, Spanish Network for Research in Infectious Diseases (grant REIPI RD16/0016/0001), cofinanced by the European Development Regional Fund A Way To Achieve Europe, Operative Program Intelligent Growth 2014-2020.; A.-S.B. received a grant from Promex Stiftung fur die Forschung (via Carigest SA) and funding from Gilead to attend the ECCMID Congress (2018). O.R.S. received speaker honoraria from MSD, Astellas, Novartis, and Pfizer. S.S.K. received speaker honoraria from Pfizer, MSD, Astellas. F.H. received speaker honoraria from MSD, and Pfizer and a research and educational grant from Pfizer. the rest of the authors declare no conflicts of interest.