Publication: Clinical Predictive Model of Multidrug Resistance in Neutropenic Cancer Patients with Bloodstream Infection Due to Pseudomonas aeruginosa.
Loading...
Identifiers
Date
2020-01-25
Authors
Gudiol, C
Albasanz-Puig, A
Laporte-Amargos, J
Pallares, N
Mussetti, A
Ruiz-Camps, I
Puerta-Alcalde, P
Abdala, E
Oltolini, C
Akova, M
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
American Society for Microbiology
Abstract
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.
Description
MeSH Terms
Anti-bacterial agents
Bacteremia
Drug resistance, multiple, bacterial
Female
Humans
Male
Microbial sensitivity tests
Middle aged
Models, biological
Neoplasms
Neutropenia
Pseudomonas infections
Pseudomonas aeruginosa
ROC curve
Retrospective studies
Risk factors
Treatment outcome
Bacteremia
Drug resistance, multiple, bacterial
Female
Humans
Male
Microbial sensitivity tests
Middle aged
Models, biological
Neoplasms
Neutropenia
Pseudomonas infections
Pseudomonas aeruginosa
ROC curve
Retrospective studies
Risk factors
Treatment outcome
DeCS Terms
Antibacterianos
Curva ROC
Farmacorresistencia bacteriana múltiple
Infecciones por Pseudomonas
Modelos biológicos
Neoplasias
Neutropenia
Pruebas de sensibilidad microbiana
Curva ROC
Farmacorresistencia bacteriana múltiple
Infecciones por Pseudomonas
Modelos biológicos
Neoplasias
Neutropenia
Pruebas de sensibilidad microbiana
CIE Terms
Keywords
Pseudomonas aeruginosa, Bacteremia, Bloodstream infection, Cancer, Multidrug resistant, Neutropenia, Predictive model, Risk factors
Citation
Gudiol C, Albasanz-Puig A, Laporte-Amargós J, Pallarès N, Mussetti A, Ruiz-Camps I, et al. Clinical Predictive Model of Multidrug Resistance in Neutropenic Cancer Patients with Bloodstream Infection Due to Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2020 Mar 24;64(4):e02494-19