Journal of Medical Microbiology Papers in Press. Published September 26, 2014 as doi:10.1099/jmm.0.073262-0 1
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Pseudomonas aeruginosa bacteremia: independent risk factors for mortality and
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impact of resistance on outcome.
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Resistant Pseudomonas aeruginosa bacteremia
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Raquel Cavalcanti Dantas1,*;Melina Lorraine Ferreira1; Paulo Pinto Gontijo-Filho1;
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Rosineide Marques Ribas1
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Dantas RC; Gontijo-Filho PP; Ferreira ML; Ribas RM
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1
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Gerais, Brazil.
Laboratory of Microbiology, Federal University of Uberlandia, Uberlandia, Minas
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*Corresponding author address: Avenida Pará, n.1720, Bairro Umuarama, Uberlândia, Minas Gerais, Brasil. CEP: 38400-902. Tel.: (+55) 34-3218-2236; E-mail address: [email protected]
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The rates of multidrug-resistant (MDR), extensively drug-resistant (XDR), and
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pandrug-resistant (PDR) isolates among non-fermenting gram-negative bacilli,
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particularly Pseudomonas aeruginosa, have risen worldwide. The clinical consequence
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of resistance and the impact of adverse treatment on the outcome of patients with P.
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aeruginosa bacteremia remain unclear. To better understand the predictors of mortality,
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the clinical consequence of resistance and the impact of inappropriate therapy on
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patients outcomes, we analyzed the first episode of P. aeruginosa bacteremia in patients
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from a Brazilian tertiary care hospital during the period of May 2009, to August 2011.
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Antimicrobial susceptibility testing was conducted; the phenotypic detection of metallo-
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β-lactamase (MBL) and the polymerase chain reaction of MBL genes were performed
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on carbapenem-resistant strains. Among the 120 P. aeruginosa isolates, 45.8% were
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resistant to carbapenem, and 36 strains were tested for MBL detection. A total of 30%
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were phenotypically positive, and of these, 77.8% expressed an MBL gene, blaSPM-1
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(57%) and blaVIM-type (43%). The resistance rates to ceftazidime, cefepime,
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piperacillin-tazobactam, carbapenem, fluoroquinolone, or aminoglycoside were 55%,
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42.5%, 35%, 45.8%, 44% and 44%, respectively. The previous antibiotic use, length of
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a hospital stay ≥ 30 days prior to P. aeruginosa, hemodialysis, tracheostomy, pulmonary
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source of bacteremia and intensive care unit admission were common independent risk
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factors for antimicrobial resistance. The cefepime-resistance, MDR and XDR were
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independently associated with inappropriate therapy, which was an important predictor
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of mortality, being synergistic with the severity of the underlying disease.
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Introduction
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Pseudomonas aeruginosa is an ubiquitous environmental bacterium with
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minimal requirements for survival and a remarkable ability to adapt to a variety of
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environmental challenges (Singh et al., 2010; Blanc et al., 2007). This pathogenic
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plasticity is attributed to the massive genome associated with flexible metabolism and
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the low permeability of the outer membrane, making this pathogen resistant to a large
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range of harmful agents, including antibiotics (Caulcott et al., 1984; Mathee et al.,
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2008). The resistance to antimicrobial drugs is increasingly becoming an important
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health and economic problem and P. aeruginosa, one of the main microorganisms of
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nosocomial infections, is known for its resistance to a range of antimicrobial agents
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(Morales et al., 2012; Hirsch et al., 2010).
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The impact of antimicrobial resistance on clinical and economic outcomes is the
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subject of ongoing investigation, (Crosgrove et al., 2006) but many studies have shown
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its negative impact on effective antimicrobial therapy. Inappropriate empirical therapy
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has been associated with increased mortality in resistant P. aeruginosa infections,
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delays in starting appropriate therapy may contribute to increased length of hospital stay
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and persistence of infection. In addition, worse clinical outcomes may be associated
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with resistant infections owing to antimicrobial options of limited efficacy (Tam et al.,
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2010; Kang et al., 2003; Kang et al., 2005; Lodise et al., 2007).
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P. aeruginosa resistance to antipseudomonal drugs has increased in several
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regions of the world (Andrade et al., 2003; Sader et al., 2001; Kiffer et al., 2005; Souli
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et al., 2008) and the Centers for Disease Control and Prevention (CDC)—with the aim
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of enhancing the comparability of data and of promoting better comprehension of the
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problem of highly drug-resistant bacteria, recently created standardized international
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definitions for multidrug-resistant (MDR), extensively drug-resistant (XDR), and
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pandrug-resistant (PDR). MDR was defined as non-susceptibility to at least one agent in
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three or more antimicrobial categories; XDR was defined as non-susceptibility to at
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least one agent in all but two or fewer antimicrobial categories; and PDR was defined as
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non-susceptibility to all agents in all antimicrobial categories (Magiorakos et al, 2012).
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Carbapenems have been considered first-line agents for the treatment of P.
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aeruginosa infections, mainly in severe cases; however the resistance of P. aeruginosa
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to these drugs has reached more than 60% in some Brazilian hospitals (Kiffer et al.,
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2005; Baumgart et al., 2010). Previous studies of clinical P. aeruginosa isolates have
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reported that resistance to carbapenems resulted from the complex interaction of several
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mechanisms including the production of carbapenemase activity, usually a metallo-β-
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lactamase (MBL), the loss of the OprD porin, and the overexpression of efflux systems
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(Xavier et al., 2010; El Amin et al., 2005). The MBL production corresponded with up
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to 30% in previous studies of Brazilian hospitals, which have shown SPM-1 as the most
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prevalent enzyme (Wirth et al., 2009; Scheffer et al., 2010; Strateva et al., 2009).
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Only a few studies have examined the clinical consequences of antibiotic
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resistance and the impact of inappropriate antimicrobial therapy on the outcomes of
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patients with P. aeruginosa bacteremia. Thus, this study was performed to evaluate
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these factors and to identify the predictors of mortality in patients with MDR, XDR, and
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PDR P. aeruginosa bacteremia. In addition, we also detected the risk factors associated
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with different antibiotic resistance and MBL-producing P. aeruginosa that showed
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carbapenem resistance.
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Methods
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Patients and setting: The database at our clinical microbiology laboratory was
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reviewed to identify patients with P. aeruginosa bacteremia from May 2009 to August
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2011 at Uberlandia University Hospital (Brazil), a 530-bed tertiary care university
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hospital. Only the first episode was analyzed for those patients with > 1 episode of
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bacteremia.
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Study design and data collection: A retrospective study was employed to
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identify the predictors of mortality and to evaluate the clinical consequence of resistance
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and the impact of inappropriate therapy on outcomes of patients with P. aeruginosa
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bacteremia. The main outcome was in-hospital mortality, and the measure used was a
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30-day mortality rate. We also assessed secondary outcomes, including the duration of
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hospital stay, admission to the Intensive Care Unit (ICU), and use of invasive
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procedures at collection of P. aeruginosa blood culture. Data from patients with
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antimicrobial-resistant P. aeruginosa bacteremia were compared with those from
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patients with susceptible bacteremia in an effort to determine factors associated with
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resistance. For each study patient, the following characteristics were recovered in
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clinical records: age; gender; length of total hospital stay; length of hospital stay before
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P. aeruginosa bacteremia; admission to ICU; surgery; invasive procedures such as
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mechanical
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hemodialysis, catheter for enteral or gastric nutrition, and surgical drain during the
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current hospitalization. The medical histories of the patient’s underlying conditions such
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as diabetes mellitus, chronic renal failure, heart failure, Acquired Immune Deficiency
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Syndrome (AIDS), and cancer. We also assessed the sources of bacteremia, previous
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antibiotic use during current hospitalization and cases of inadequate antimicrobial
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treatment.
ventilation,
central
venous
line,
urinary catheter,
tracheostomy,
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Clinical microbiological and molecular testing: Cultures were processed using
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BACT/Alert® (bioMérieux, Durham, NC, USA). Microbial identification and
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antimicrobial susceptibility testing were performed on the VITEK-2® automated system
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(bioMérieux, Durham, NC, USA) for the following antimicrobials: ceftazidime,
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cefepime, piperacillin-tazobactam, carbapenem (imipenem and/or merepenem),
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fluoroquinolone (ciprofloxacin and/or levofloxacin), aminoglycosides (gentamicin
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and/or amikacin), aztreonam and polymyxin B. P. aeruginosa strains that showed
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reduced susceptibility were included in the resistant group. Quality-control protocols
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were used according to the standards of the Clinical and Laboratory Standard Institute
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(CLSI, 2009; CLSI, 2010; CLSI, 2011). The carbapenem-resistant P. aeruginosa
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isolates were phenotypically screened for MBL production using double-disk synergy
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tests, as previously described (Arakawa et al., 2000). In addition, to assess the presence
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of MBL genes in P. aeruginosa strains, a multiplex PCR was performed, as previously
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described (Woodford, 2010). The cycling conditions were as follows: 94 oC for 5 min,
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followed by 30 denaturation cycles at 94 oC for 30 sec; annealing at 53oC for 45 sec;
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and extension at 72 oC for 30 sec, followed by final extension at 72 oC at 10 min; all in a
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MasterCycler® personal (Eppendorf ®).
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Definitions: According to the Centers for Disease Control and Prevention
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(CDC), Bacteremia is defined as the presence of viable microorganisms in the
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bloodstream documented by a positive blood culture result. Bacteremia was considered
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to be nosocomial if the infection occurred > 48 h after admission and no clinical
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evidence of infection on admission existed (Horan et al., 2008). Bacteremia was
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classified as primary when patient had a recognized pathogen cultured from one or more
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blood cultures and the organism cultured from blood is not related to an infection at
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another site. Catheter-related bacteremia was defined as when the patient had an
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intravascular device and ≥ 1 positive blood culture result obtained from the peripheral
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vein and no apparent source for infection except the catheter. Bacteremia was
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considered secondary when the patient has a recognized pathogen cultured from one or
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more blood cultures and organism cultured from blood is related to an infection at
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another site (Horan et al, 2008). Outcomes were classified as either death or survival,
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but no attempt was made to determine if death was directly attributable to P. aeruginosa
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bacteremia. For the comparison of outcomes, survival status was evaluated at 30 days
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after the onset of bacteremia (Lodise et al., 2007). The Average Severity of Illness
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Score (ASIS) was also recorded for each patient using the CDC National Nosocomial
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Infections Surveillance NNIS System criteria (Emori et al., 1991; Rosenthal et al.,
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2006). The empirical antimicrobial therapy was considered to be “appropriate” if the
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initial antibiotics, which were administered within 24 h after the acquisition of a blood
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culture sample, included at least one antibiotic was active in vitro (Gilbert et al., 2007).
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We considered that patients to have prior antibiotic therapy if they had received an
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antimicrobial agent for at least 2 days within 30 days preceding the onset of P.
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aeruginosa bacteremia.
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Statistical analysis: The Student’s t test was used to compare continuous
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variables, and X2 or Fisher’s exact test was used to compare categorical variables. To
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determine independent risk factors for 30-day mortality and resistance, a multiple
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logistic regression model was used to control for the effects of confounding variables.
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Variables with P < 0.05 in the univariate analysis were candidates for multivariate
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analysis. Survival curve was constructed by means of the Kaplan-Meier method, and the
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log rank test was used for comparisons between patients with inappropriate and
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adequate therapy. All P values were two tailed, and P values < 0.05 were considered to
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be statistically significant.
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Ethical approval: The research Ethics Committee of the Federal University Uberlandia
evaluated
and
approved
our
study
design.
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Results
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Study population and mortality predictors: From May 1, 2009, to August 31,
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2011, a total of 120 non-repetitive patients with P. aeruginosa bacteremia at the
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university hospital were included in the study. The detailed information on factors
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associated with death and the relevant demographic and clinical characteristics of the
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study population are summarized in Table 1. The proportion of males and females were
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63.3% (N= 76) and 36.7% (N= 44), respectively. The median age of the patients was
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51.5 ± 3.2 (range 0 to 89 years). The majority of the patients were from surgical and
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medical wards (28.3% and 31.6%, respectively). The sources of infection included
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bacteremia of an unknown origin in 60.8%, bacteremia episodes related to intravascular
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catheters in 16.7%, and bacteremia associated with the lungs in 14.2%. Results of a
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multivariate analysis for an association between cohort risk factors and hospital
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mortality have shown that the predictors that are independently associated with death
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were patients with severe underlying disease, such as cancer and/or the AIDS, as well as
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patients who received inadequate antimicrobial therapy. The median length of a hospital
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stay after admission was 32 days for survivors and 79 days for patients who died. The
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Kaplan-Meier cumulative survival estimates (Fig. 1) for patients with inappropriate
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versus appropriate therapy show that the group that received incorrect therapy had a
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lower probability of survival than the appropriate therapy group does (P = 0,001). The
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30-day mortality rate of the first group was 48.0%, whereas that of the second group
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was 14.3%. The severity of the patients was accessed through ASIS scores (Table 1),
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and no significant difference was found between survivors and patients who died.
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Risk factors associated with antimicrobial resistance and treatment outcome:
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Antimicrobial susceptibility testing results were analyzed and 58 strains (48.3%)
9
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behaved as MDR isolates, 31 (25.8%) as XDR isolates, none behaved as PDR isolates,
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and 31 (25.8%) were other profiles. The resistance rates to ceztazidime, cefepime,
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piperacillin-tazobactam, carbapenem, fluoroquinolones, and aminoglycosides were 55%
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(66/120), 42.5% (51/120), 35.8% (43/120), 45.8% (55/120), 45% (54/120), and 45%
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(54/120), respectively. The risk factors associated with resistance to each class of
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antimicrobials were evaluated, and the independent variables associated with the
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respective resistances are shown in Table 2. The common risk factors for antimicrobial
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resistance were as follows: previous antibiotic use, mainly carbapenems and
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fluoroquinolones, hemodialysis, tracheostomy, hospitalization up to 30-days before P.
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aeruginosa and pulmonary source of bacteremia.
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The antibiotic therapy was evaluated and patients with bacteremia stemming
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from resistant P. aeruginosa had higher inappropriate therapy rates than did those
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patients with susceptible bacteremia, particularly to the cefepime-resistant (P = 0.02),
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MDR (P = 0.009) and XDR (P = 0.03) groups. Overall, when the clinical outcome was
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assessed, the 30-day mortality rate and time of hospital stay were higher to the resistant
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groups when compared with susceptible groups (Table 3).
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MBL production: The MBL production was conducted for 36 carbapenem-
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resistant P. aeruginosa isolates. Nine (25.0%) isolates were phenotypically positive and
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7 (19.4%) presented an amplicon consistent with MBL genes, being blaSPM-1 in 57%
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(4/7) and blaVIM-type in 43% (3/7). Based on the antibiotic susceptibility testing, 6
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antibiotypes (R1-R6) were identified among isolates that were phenotypically positive
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for MBL production. Two isolates were assigned antibiotype R1 and were XDR-P.
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aeruginosa (resistant to all antibiotics tested, with the exception of polymyxin B). The
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other antibiotypes (R2-R6) fit the MDR profile (Table 4).
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Discussion
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Antimicrobial resistance is a worldwide problem with severe implications in
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developing countries due to the rapid emergence and spread of antimicrobial-resistant
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non-fermentative gram-negative bacilli, especially Pseudomonas aeruginosa isolates,
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thus posing a considerable threat to public health (Morales et al., 2012; Hirsch et al.,
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2010; Kiffer et al., 2005; Souli et al., 2008; Baumgart et al., 2010; Xavier et al., 2010;
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Scheffer et al., 2010). To our knowledge, this work represents the first comprehensive
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assessment in Brazil of bacteremia stemming from antimicrobial-resistant P.
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aeruginosa, inappropriate therapy and death in a tertiary care population.
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It is often assumed that bacteremia caused by antimicrobial-resistant P.
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aeruginosa results in higher rates of mortality due to the negative effect of the delay of
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the administration of an appropriate antibiotic therapy as well as the administration of
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an inactive antimicrobial (Kang et al., 2003; Kang et al., 2005; Lodise et al., 2007).
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From our cohort of 120 patients with P. aeruginosa bacteremia, 57 had a MDR profile
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and 31 had a XDR profile and the presence of these isolates was significantly associated
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with inappropriate antimicrobial therapy, which was an independent predictor of death.
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This aspect has been stressed in patients with serious infections in tertiary care,
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due to countless different risk factors to occurrence of infection by resistant strains (Joo
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et al., 2011; Peña et al., 2012). In our study, the presence of pneumonia and
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hospitalization in the ICU were independent risk factors for developing bacteremia due
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to MDR or XDR-P. aeruginosa. Several risk factors for antimicrobial resistance were
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statistically significant, however, after adjusting the variables, the independent risk
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factors identified were, mainly, the prior antimicrobial therapy and a length of stay > 30
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days prior to infection.
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Zavascki et al., 2006 showed that MBL-producing P. aeruginosa increases the
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risk of incorrect therapy, thus increasing the mortality. This aspect was not assessed in
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our study, but we detected a relatively high percentage (77.8%; 7/9) of MBL genes
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among phenotypically positive isolates, of which 57% (4/7) blaSPM-1 and 43% (3/7)
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blaVIM-type. However, most of our carbapenem-resistant isolates (80,5%; 29/36) were
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negative for MBL genes, suggesting the existence of other metallo-β-lactamase not
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tested and other drug-resistance mechanisms such as the upregulation of intrinsic
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cephalosporinase AmpC production, overexpression of efflux systems and outer
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membrane impermeability (porin loss) (Xavier et al., 2010). These results revealed an
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important change in the epidemiology of carbapenem-resistant P. aeruginosa isolates
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between the periods of 2005 (Cezário et al., 2009) and 2011 in our hospital, revealing
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an increasing prevalence of the VIM-MBL-producing isolates in the final period of
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study.
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Several retrospective works, including regional studies from Brazil, have
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evaluated the mortality of patients with P. aeruginosa bacteremia and some of these
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showed that the impact of antimicrobial resistance on patient outcomes might depend on
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the severity of underlying conditions, primary source of infection and incorrect therapy
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(Kang et al., 2003; Kang et al., 2005; Furtado et al., 2009). Our study further confirmed
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the notion that patients who are infected with antimicrobial resistant P. aeruginosa
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isolates normally have worse treatment outcomes and has been found to be a strong
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prognostic factor for mortality, even after adjusting for the severity of illness and the
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underlying condition (Joo et al., 2011).
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Like our finding, previous studies showed that P. aeruginosa isolated from
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patients who had been exposed to previous antimicrobial therapy prior to the
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development of bacteremia had an increased risk of being resistant to different classes
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of antibiotics (Joo et al., 2011; Peña et al., 2012). Unlike in other studies, the frequency
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of ceftazidime (55%) piperacillin-tazobactam (35%), and fluoroquinolone resistance
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(44%) was much higher, suggesting the existence of different resistance mechanisms.
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We also highlight the frequent use of cephalosporins and fluoroquinolones in our
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hospital. All isolates were susceptible to polymyxin B, which resurfaces for the
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empirical treatment of P. aeruginosa infections due to lack of new effective antibiotics,
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although reports of clinical P. aeruginosa isolates with reduced susceptibility to this
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antimicrobial class have been reported, including strains producing of MBL (Franco et
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al., 2010; Laupland et al., 2005).
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In summary, our results indicate that a high incidence of MDR and XDR isolates
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exists among hospitalized patients with bacteremia. In addition, our data suggest that
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antimicrobial resistance, especially cefepime-resistant isolates and MDR or XDR
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strains, adversely affect outcomes in patients with P. aeruginosa bacteremia through
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inadequate therapy. Thus, the monitoring of resistant P. aeruginosa isolates and the
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knowledge of the factors associated with this resistance becomes important in order to
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prevent the emergence of pandrug resistance among clinical isolates of P. aeruginosa in
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our hospital.
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Acknowledgments The authors thank the Brazilian Agency CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, for its financial support.
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Galagan, J. E., Lory, S. Dynamics of Pseudomonas aeruginosa genome evolution. 8,
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infections in 55 intensive care units of 8 developing countries. Ann Intern Med 145,
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Sader, H. S., Gales, A. C., Pfaller, M. A., Mendes, R. E., Zoccoli, C., Barth, A. &
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summary of results from three years of the SENTRY Antimicrobial Surveillance
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Scheffer, M. C., Gales, A. C., Barth, A. L., Carmo Filho, J. R. & Dalla-Costa, L. M.
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Brazil and in the state of Goias. Braz J Infect Dis 14, 508–509.
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457
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21
458
Table 1. Characteristics and risk factors associated with 30-day mortality of patients with bacteremia caused by Pseudomonas aeruginosa Risk factors
Age-mean (years) Male / female Length of hospital stay-mean (days) Intensive Care Unit Surgery Invasive procedures 72 h Mechanical ventilation Tracheostomy Urinary catheter Central Venous Catheter Surgical drain Probes enteral or gastric nutrition Hemodialysis Parenteral nutrition Comorbidity conditions Heart failure Cancer Diabetes Mellitus Chronic renal failure Human immunodeficiency virus ASIS4 score ≥ 4 Primary bacteremia Central Line Catheter related Unknown Secondary bacteremia Respiratory tract Urinary tract Others5 Inadequate treatment 1
Total N=120 (%) 51.75 76/44 55.4 55 (45.8) 52 (43.3) 103 (85.8) 64 (53.3) 50 (41.7) 76 (63.3) 90 (75.0) 20 (16.7) 70 (58.3) 24 (20.0) 17 (14.2) 94 (78.3) 30 (25.0) 24 (20.0) 14 (11.7) 28 (23.3) 9 (7.5) 67 (55.8) 93 (77.5) 20 (16.7) 73 (60.8) 27 (22.5) 17 (14.2) 5 (4.2) 5 (4.2) 34 (28.3)
Survival N= 70 (%) 55.8±3.1 47/23 (67.1/32.9) 31.78±3.8 36 (51.4) 30 (42.8) 60 (85.7) 36 (51.4) 28 (40.0) 42 (60.0) 51 (72.9) 10 (14.3) 41 (58.6) 6 (8.5) 10 (14.3) 48 (68.7) 16 (22.9) 6 (8.6) 6 (8.6) 9 (12.9) 2 (2.9) 36 (51.4) 58 (82.9 15 (21.4) 43 (61.4) 12 (17.1) 7 (10.0) 3 (4.3) 2 (2.9) 10 (14.3)
Death N=50 (%) 47.1±3.2 29/21 (58.0/42.0) 79.01±8.4 19 (38.0) 22 (44.0) 43 (86.0) 28 (56.0) 22 (44.0) 34 (68.0) 39 (78.0) 10 (20.0) 29 (58.0) 18 (36.0) 7 (14.0) 46 (92.0) 14 (28.0) 18 (36.0) 8 (16.0) 19 (38.0) 7 (14.0) 31 (62.0) 35 (70.0) 5 (10.0) 30 (60.0) 15 (30.0) 10 (20.0) 2 (4.0) 3 (6.0) 24 (48.0)
Univariate OR1 (IC2 95%) 0.68 (0.30-1.53) 0.58 (0.26-1.29) 1.05 (0.47-2.32) 1.02 (0.32-3.28) 1.20 (0.54-2.66) 1.18 (0.53-2.63) 1.42 (0.62-3.26) 1.32 (0.52-3.38) 1.50 (0.52-4.35) 0.98 (0.44-2.18) 6.00 (1.99-18.92) 0.98 (0.31-3.08) 5.27 (1.55-19.67) 1.31 (0.53-3.27) 6.00 (1.99-18-92) 2.03 (0.58-7.21) 4.15 (1.55-11.35) 5.53 (0.98-40.62) 1.54 (0.69-3.45) 0.48 (0.19-1.25) 0.41 (0.12-1.32) 0.94 (0.42-2.12) 2.07 (0.80-5.39) 2.25 (0.71-7.23) 0.93 (0.10-7.21) 2.17 (0.28-19.44) 5.36 (2.13-13.77)
Odds 459 rati ; 2Confidence interval; 3P value; 4; 5Ascitic fluid, cavity abscess, wound secretion, ocular secretion, liquor. *P statistically significant
P3 0.09 0.40
1
Pseudomonas aeruginosa bacteremia: independent risk factors for mortality and
2
impact of resistance on outcome.
3 4
Resistant Pseudomonas aeruginosa bacteremia
5 6 7
Raquel Cavalcanti Dantas1,*;Melina Lorraine Ferreira1; Paulo Pinto Gontijo-Filho1;
8
Rosineide Marques Ribas1
9
Dantas RC; Gontijo-Filho PP; Ferreira ML; Ribas RM
10 11
1
12
Gerais, Brazil.
Laboratory of Microbiology, Federal University of Uberlandia, Uberlandia, Minas
13 14 15 16 17 18 19 20 21
*Corresponding author address: Avenida Pará, n.1720, Bairro Umuarama, Uberlândia, Minas Gerais, Brasil. CEP: 38400-902. Tel.: (+55) 34-3218-2236; E-mail address: [email protected]
22
The rates of multidrug-resistant (MDR), extensively drug-resistant (XDR), and
23
pandrug-resistant (PDR) isolates among non-fermenting gram-negative bacilli,
24
particularly Pseudomonas aeruginosa, have risen worldwide. The clinical consequence
25
of resistance and the impact of adverse treatment on the outcome of patients with P.
26
aeruginosa bacteremia remain unclear. To better understand the predictors of mortality,
27
the clinical consequence of resistance and the impact of inappropriate therapy on
28
patients outcomes, we analyzed the first episode of P. aeruginosa bacteremia in patients
29
from a Brazilian tertiary care hospital during the period of May 2009, to August 2011.
30
Antimicrobial susceptibility testing was conducted; the phenotypic detection of metallo-
31
β-lactamase (MBL) and the polymerase chain reaction of MBL genes were performed
32
on carbapenem-resistant strains. Among the 120 P. aeruginosa isolates, 45.8% were
33
resistant to carbapenem, and 36 strains were tested for MBL detection. A total of 30%
34
were phenotypically positive, and of these, 77.8% expressed an MBL gene, blaSPM-1
35
(57%) and blaVIM-type (43%). The resistance rates to ceftazidime, cefepime,
36
piperacillin-tazobactam, carbapenem, fluoroquinolone, or aminoglycoside were 55%,
37
42.5%, 35%, 45.8%, 44% and 44%, respectively. The previous antibiotic use, length of
38
a hospital stay ≥ 30 days prior to P. aeruginosa, hemodialysis, tracheostomy, pulmonary
39
source of bacteremia and intensive care unit admission were common independent risk
40
factors for antimicrobial resistance. The cefepime-resistance, MDR and XDR were
41
independently associated with inappropriate therapy, which was an important predictor
42
of mortality, being synergistic with the severity of the underlying disease.
43 44 45
46
Introduction
47
Pseudomonas aeruginosa is an ubiquitous environmental bacterium with
48
minimal requirements for survival and a remarkable ability to adapt to a variety of
49
environmental challenges (Singh et al., 2010; Blanc et al., 2007). This pathogenic
50
plasticity is attributed to the massive genome associated with flexible metabolism and
51
the low permeability of the outer membrane, making this pathogen resistant to a large
52
range of harmful agents, including antibiotics (Caulcott et al., 1984; Mathee et al.,
53
2008). The resistance to antimicrobial drugs is increasingly becoming an important
54
health and economic problem and P. aeruginosa, one of the main microorganisms of
55
nosocomial infections, is known for its resistance to a range of antimicrobial agents
56
(Morales et al., 2012; Hirsch et al., 2010).
57
The impact of antimicrobial resistance on clinical and economic outcomes is the
58
subject of ongoing investigation, (Crosgrove et al., 2006) but many studies have shown
59
its negative impact on effective antimicrobial therapy. Inappropriate empirical therapy
60
has been associated with increased mortality in resistant P. aeruginosa infections,
61
delays in starting appropriate therapy may contribute to increased length of hospital stay
62
and persistence of infection. In addition, worse clinical outcomes may be associated
63
with resistant infections owing to antimicrobial options of limited efficacy (Tam et al.,
64
2010; Kang et al., 2003; Kang et al., 2005; Lodise et al., 2007).
65
P. aeruginosa resistance to antipseudomonal drugs has increased in several
66
regions of the world (Andrade et al., 2003; Sader et al., 2001; Kiffer et al., 2005; Souli
67
et al., 2008) and the Centers for Disease Control and Prevention (CDC)—with the aim
68
of enhancing the comparability of data and of promoting better comprehension of the
69
problem of highly drug-resistant bacteria, recently created standardized international
70
definitions for multidrug-resistant (MDR), extensively drug-resistant (XDR), and
71
pandrug-resistant (PDR). MDR was defined as non-susceptibility to at least one agent in
72
three or more antimicrobial categories; XDR was defined as non-susceptibility to at
73
least one agent in all but two or fewer antimicrobial categories; and PDR was defined as
74
non-susceptibility to all agents in all antimicrobial categories (Magiorakos et al, 2012).
75
Carbapenems have been considered first-line agents for the treatment of P.
76
aeruginosa infections, mainly in severe cases; however the resistance of P. aeruginosa
77
to these drugs has reached more than 60% in some Brazilian hospitals (Kiffer et al.,
78
2005; Baumgart et al., 2010). Previous studies of clinical P. aeruginosa isolates have
79
reported that resistance to carbapenems resulted from the complex interaction of several
80
mechanisms including the production of carbapenemase activity, usually a metallo-β-
81
lactamase (MBL), the loss of the OprD porin, and the overexpression of efflux systems
82
(Xavier et al., 2010; El Amin et al., 2005). The MBL production corresponded with up
83
to 30% in previous studies of Brazilian hospitals, which have shown SPM-1 as the most
84
prevalent enzyme (Wirth et al., 2009; Scheffer et al., 2010; Strateva et al., 2009).
85
Only a few studies have examined the clinical consequences of antibiotic
86
resistance and the impact of inappropriate antimicrobial therapy on the outcomes of
87
patients with P. aeruginosa bacteremia. Thus, this study was performed to evaluate
88
these factors and to identify the predictors of mortality in patients with MDR, XDR, and
89
PDR P. aeruginosa bacteremia. In addition, we also detected the risk factors associated
90
with different antibiotic resistance and MBL-producing P. aeruginosa that showed
91
carbapenem resistance.
92 93
Methods
94
Patients and setting: The database at our clinical microbiology laboratory was
95
reviewed to identify patients with P. aeruginosa bacteremia from May 2009 to August
96
2011 at Uberlandia University Hospital (Brazil), a 530-bed tertiary care university
97
hospital. Only the first episode was analyzed for those patients with > 1 episode of
98
bacteremia.
99
Study design and data collection: A retrospective study was employed to
100
identify the predictors of mortality and to evaluate the clinical consequence of resistance
101
and the impact of inappropriate therapy on outcomes of patients with P. aeruginosa
102
bacteremia. The main outcome was in-hospital mortality, and the measure used was a
103
30-day mortality rate. We also assessed secondary outcomes, including the duration of
104
hospital stay, admission to the Intensive Care Unit (ICU), and use of invasive
105
procedures at collection of P. aeruginosa blood culture. Data from patients with
106
antimicrobial-resistant P. aeruginosa bacteremia were compared with those from
107
patients with susceptible bacteremia in an effort to determine factors associated with
108
resistance. For each study patient, the following characteristics were recovered in
109
clinical records: age; gender; length of total hospital stay; length of hospital stay before
110
P. aeruginosa bacteremia; admission to ICU; surgery; invasive procedures such as
111
mechanical
112
hemodialysis, catheter for enteral or gastric nutrition, and surgical drain during the
113
current hospitalization. The medical histories of the patient’s underlying conditions such
114
as diabetes mellitus, chronic renal failure, heart failure, Acquired Immune Deficiency
115
Syndrome (AIDS), and cancer. We also assessed the sources of bacteremia, previous
116
antibiotic use during current hospitalization and cases of inadequate antimicrobial
117
treatment.
ventilation,
central
venous
line,
urinary catheter,
tracheostomy,
118
Clinical microbiological and molecular testing: Cultures were processed using
119
BACT/Alert® (bioMérieux, Durham, NC, USA). Microbial identification and
120
antimicrobial susceptibility testing were performed on the VITEK-2® automated system
121
(bioMérieux, Durham, NC, USA) for the following antimicrobials: ceftazidime,
122
cefepime, piperacillin-tazobactam, carbapenem (imipenem and/or merepenem),
123
fluoroquinolone (ciprofloxacin and/or levofloxacin), aminoglycosides (gentamicin
124
and/or amikacin), aztreonam and polymyxin B. P. aeruginosa strains that showed
125
reduced susceptibility were included in the resistant group. Quality-control protocols
126
were used according to the standards of the Clinical and Laboratory Standard Institute
127
(CLSI, 2009; CLSI, 2010; CLSI, 2011). The carbapenem-resistant P. aeruginosa
128
isolates were phenotypically screened for MBL production using double-disk synergy
129
tests, as previously described (Arakawa et al., 2000). In addition, to assess the presence
130
of MBL genes in P. aeruginosa strains, a multiplex PCR was performed, as previously
131
described (Woodford, 2010). The cycling conditions were as follows: 94 oC for 5 min,
132
followed by 30 denaturation cycles at 94 oC for 30 sec; annealing at 53oC for 45 sec;
133
and extension at 72 oC for 30 sec, followed by final extension at 72 oC at 10 min; all in a
134
MasterCycler® personal (Eppendorf ®).
135
Definitions: According to the Centers for Disease Control and Prevention
136
(CDC), Bacteremia is defined as the presence of viable microorganisms in the
137
bloodstream documented by a positive blood culture result. Bacteremia was considered
138
to be nosocomial if the infection occurred > 48 h after admission and no clinical
139
evidence of infection on admission existed (Horan et al., 2008). Bacteremia was
140
classified as primary when patient had a recognized pathogen cultured from one or more
141
blood cultures and the organism cultured from blood is not related to an infection at
142
another site. Catheter-related bacteremia was defined as when the patient had an
143
intravascular device and ≥ 1 positive blood culture result obtained from the peripheral
144
vein and no apparent source for infection except the catheter. Bacteremia was
145
considered secondary when the patient has a recognized pathogen cultured from one or
146
more blood cultures and organism cultured from blood is related to an infection at
147
another site (Horan et al, 2008). Outcomes were classified as either death or survival,
148
but no attempt was made to determine if death was directly attributable to P. aeruginosa
149
bacteremia. For the comparison of outcomes, survival status was evaluated at 30 days
150
after the onset of bacteremia (Lodise et al., 2007). The Average Severity of Illness
151
Score (ASIS) was also recorded for each patient using the CDC National Nosocomial
152
Infections Surveillance NNIS System criteria (Emori et al., 1991; Rosenthal et al.,
153
2006). The empirical antimicrobial therapy was considered to be “appropriate” if the
154
initial antibiotics, which were administered within 24 h after the acquisition of a blood
155
culture sample, included at least one antibiotic was active in vitro (Gilbert et al., 2007).
156
We considered that patients to have prior antibiotic therapy if they had received an
157
antimicrobial agent for at least 2 days within 30 days preceding the onset of P.
158
aeruginosa bacteremia.
159
Statistical analysis: The Student’s t test was used to compare continuous
160
variables, and X2 or Fisher’s exact test was used to compare categorical variables. To
161
determine independent risk factors for 30-day mortality and resistance, a multiple
162
logistic regression model was used to control for the effects of confounding variables.
163
Variables with P < 0.05 in the univariate analysis were candidates for multivariate
164
analysis. Survival curve was constructed by means of the Kaplan-Meier method, and the
165
log rank test was used for comparisons between patients with inappropriate and
166
adequate therapy. All P values were two tailed, and P values < 0.05 were considered to
167
be statistically significant.
168 169
Ethical approval: The research Ethics Committee of the Federal University Uberlandia
evaluated
and
approved
our
study
design.
8
170
Results
171
Study population and mortality predictors: From May 1, 2009, to August 31,
172
2011, a total of 120 non-repetitive patients with P. aeruginosa bacteremia at the
173
university hospital were included in the study. The detailed information on factors
174
associated with death and the relevant demographic and clinical characteristics of the
175
study population are summarized in Table 1. The proportion of males and females were
176
63.3% (N= 76) and 36.7% (N= 44), respectively. The median age of the patients was
177
51.5 ± 3.2 (range 0 to 89 years). The majority of the patients were from surgical and
178
medical wards (28.3% and 31.6%, respectively). The sources of infection included
179
bacteremia of an unknown origin in 60.8%, bacteremia episodes related to intravascular
180
catheters in 16.7%, and bacteremia associated with the lungs in 14.2%. Results of a
181
multivariate analysis for an association between cohort risk factors and hospital
182
mortality have shown that the predictors that are independently associated with death
183
were patients with severe underlying disease, such as cancer and/or the AIDS, as well as
184
patients who received inadequate antimicrobial therapy. The median length of a hospital
185
stay after admission was 32 days for survivors and 79 days for patients who died. The
186
Kaplan-Meier cumulative survival estimates (Fig. 1) for patients with inappropriate
187
versus appropriate therapy show that the group that received incorrect therapy had a
188
lower probability of survival than the appropriate therapy group does (P = 0,001). The
189
30-day mortality rate of the first group was 48.0%, whereas that of the second group
190
was 14.3%. The severity of the patients was accessed through ASIS scores (Table 1),
191
and no significant difference was found between survivors and patients who died.
192
Risk factors associated with antimicrobial resistance and treatment outcome:
193
Antimicrobial susceptibility testing results were analyzed and 58 strains (48.3%)
9
194
behaved as MDR isolates, 31 (25.8%) as XDR isolates, none behaved as PDR isolates,
195
and 31 (25.8%) were other profiles. The resistance rates to ceztazidime, cefepime,
196
piperacillin-tazobactam, carbapenem, fluoroquinolones, and aminoglycosides were 55%
197
(66/120), 42.5% (51/120), 35.8% (43/120), 45.8% (55/120), 45% (54/120), and 45%
198
(54/120), respectively. The risk factors associated with resistance to each class of
199
antimicrobials were evaluated, and the independent variables associated with the
200
respective resistances are shown in Table 2. The common risk factors for antimicrobial
201
resistance were as follows: previous antibiotic use, mainly carbapenems and
202
fluoroquinolones, hemodialysis, tracheostomy, hospitalization up to 30-days before P.
203
aeruginosa and pulmonary source of bacteremia.
204
The antibiotic therapy was evaluated and patients with bacteremia stemming
205
from resistant P. aeruginosa had higher inappropriate therapy rates than did those
206
patients with susceptible bacteremia, particularly to the cefepime-resistant (P = 0.02),
207
MDR (P = 0.009) and XDR (P = 0.03) groups. Overall, when the clinical outcome was
208
assessed, the 30-day mortality rate and time of hospital stay were higher to the resistant
209
groups when compared with susceptible groups (Table 3).
210
MBL production: The MBL production was conducted for 36 carbapenem-
211
resistant P. aeruginosa isolates. Nine (25.0%) isolates were phenotypically positive and
212
7 (19.4%) presented an amplicon consistent with MBL genes, being blaSPM-1 in 57%
213
(4/7) and blaVIM-type in 43% (3/7). Based on the antibiotic susceptibility testing, 6
214
antibiotypes (R1-R6) were identified among isolates that were phenotypically positive
215
for MBL production. Two isolates were assigned antibiotype R1 and were XDR-P.
216
aeruginosa (resistant to all antibiotics tested, with the exception of polymyxin B). The
217
other antibiotypes (R2-R6) fit the MDR profile (Table 4).
10
218
Discussion
219
Antimicrobial resistance is a worldwide problem with severe implications in
220
developing countries due to the rapid emergence and spread of antimicrobial-resistant
221
non-fermentative gram-negative bacilli, especially Pseudomonas aeruginosa isolates,
222
thus posing a considerable threat to public health (Morales et al., 2012; Hirsch et al.,
223
2010; Kiffer et al., 2005; Souli et al., 2008; Baumgart et al., 2010; Xavier et al., 2010;
224
Scheffer et al., 2010). To our knowledge, this work represents the first comprehensive
225
assessment in Brazil of bacteremia stemming from antimicrobial-resistant P.
226
aeruginosa, inappropriate therapy and death in a tertiary care population.
227
It is often assumed that bacteremia caused by antimicrobial-resistant P.
228
aeruginosa results in higher rates of mortality due to the negative effect of the delay of
229
the administration of an appropriate antibiotic therapy as well as the administration of
230
an inactive antimicrobial (Kang et al., 2003; Kang et al., 2005; Lodise et al., 2007).
231
From our cohort of 120 patients with P. aeruginosa bacteremia, 57 had a MDR profile
232
and 31 had a XDR profile and the presence of these isolates was significantly associated
233
with inappropriate antimicrobial therapy, which was an independent predictor of death.
234
This aspect has been stressed in patients with serious infections in tertiary care,
235
due to countless different risk factors to occurrence of infection by resistant strains (Joo
236
et al., 2011; Peña et al., 2012). In our study, the presence of pneumonia and
237
hospitalization in the ICU were independent risk factors for developing bacteremia due
238
to MDR or XDR-P. aeruginosa. Several risk factors for antimicrobial resistance were
239
statistically significant, however, after adjusting the variables, the independent risk
240
factors identified were, mainly, the prior antimicrobial therapy and a length of stay > 30
241
days prior to infection.
11
242
Zavascki et al., 2006 showed that MBL-producing P. aeruginosa increases the
243
risk of incorrect therapy, thus increasing the mortality. This aspect was not assessed in
244
our study, but we detected a relatively high percentage (77.8%; 7/9) of MBL genes
245
among phenotypically positive isolates, of which 57% (4/7) blaSPM-1 and 43% (3/7)
246
blaVIM-type. However, most of our carbapenem-resistant isolates (80,5%; 29/36) were
247
negative for MBL genes, suggesting the existence of other metallo-β-lactamase not
248
tested and other drug-resistance mechanisms such as the upregulation of intrinsic
249
cephalosporinase AmpC production, overexpression of efflux systems and outer
250
membrane impermeability (porin loss) (Xavier et al., 2010). These results revealed an
251
important change in the epidemiology of carbapenem-resistant P. aeruginosa isolates
252
between the periods of 2005 (Cezário et al., 2009) and 2011 in our hospital, revealing
253
an increasing prevalence of the VIM-MBL-producing isolates in the final period of
254
study.
255
Several retrospective works, including regional studies from Brazil, have
256
evaluated the mortality of patients with P. aeruginosa bacteremia and some of these
257
showed that the impact of antimicrobial resistance on patient outcomes might depend on
258
the severity of underlying conditions, primary source of infection and incorrect therapy
259
(Kang et al., 2003; Kang et al., 2005; Furtado et al., 2009). Our study further confirmed
260
the notion that patients who are infected with antimicrobial resistant P. aeruginosa
261
isolates normally have worse treatment outcomes and has been found to be a strong
262
prognostic factor for mortality, even after adjusting for the severity of illness and the
263
underlying condition (Joo et al., 2011).
264
Like our finding, previous studies showed that P. aeruginosa isolated from
265
patients who had been exposed to previous antimicrobial therapy prior to the
12
266
development of bacteremia had an increased risk of being resistant to different classes
267
of antibiotics (Joo et al., 2011; Peña et al., 2012). Unlike in other studies, the frequency
268
of ceftazidime (55%) piperacillin-tazobactam (35%), and fluoroquinolone resistance
269
(44%) was much higher, suggesting the existence of different resistance mechanisms.
270
We also highlight the frequent use of cephalosporins and fluoroquinolones in our
271
hospital. All isolates were susceptible to polymyxin B, which resurfaces for the
272
empirical treatment of P. aeruginosa infections due to lack of new effective antibiotics,
273
although reports of clinical P. aeruginosa isolates with reduced susceptibility to this
274
antimicrobial class have been reported, including strains producing of MBL (Franco et
275
al., 2010; Laupland et al., 2005).
276
In summary, our results indicate that a high incidence of MDR and XDR isolates
277
exists among hospitalized patients with bacteremia. In addition, our data suggest that
278
antimicrobial resistance, especially cefepime-resistant isolates and MDR or XDR
279
strains, adversely affect outcomes in patients with P. aeruginosa bacteremia through
280
inadequate therapy. Thus, the monitoring of resistant P. aeruginosa isolates and the
281
knowledge of the factors associated with this resistance becomes important in order to
282
prevent the emergence of pandrug resistance among clinical isolates of P. aeruginosa in
283
our hospital.
284 285 286 287 288
Acknowledgments The authors thank the Brazilian Agency CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, for its financial support.
13
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Table 1. Characteristics and risk factors associated with 30-day mortality of patients with bacteremia caused by Pseudomonas aeruginosa Risk factors
Age-mean (years) Male / female Length of hospital stay-mean (days) Intensive Care Unit Surgery Invasive procedures 72 h Mechanical ventilation Tracheostomy Urinary catheter Central Venous Catheter Surgical drain Probes enteral or gastric nutrition Hemodialysis Parenteral nutrition Comorbidity conditions Heart failure Cancer Diabetes Mellitus Chronic renal failure Human immunodeficiency virus ASIS4 score ≥ 4 Primary bacteremia Central Line Catheter related Unknown Secondary bacteremia Respiratory tract Urinary tract Others5 Inadequate treatment 1
Total N=120 (%) 51.75 76/44 55.4 55 (45.8) 52 (43.3) 103 (85.8) 64 (53.3) 50 (41.7) 76 (63.3) 90 (75.0) 20 (16.7) 70 (58.3) 24 (20.0) 17 (14.2) 94 (78.3) 30 (25.0) 24 (20.0) 14 (11.7) 28 (23.3) 9 (7.5) 67 (55.8) 93 (77.5) 20 (16.7) 73 (60.8) 27 (22.5) 17 (14.2) 5 (4.2) 5 (4.2) 34 (28.3)
Survival N= 70 (%) 55.8±3.1 47/23 (67.1/32.9) 31.78±3.8 36 (51.4) 30 (42.8) 60 (85.7) 36 (51.4) 28 (40.0) 42 (60.0) 51 (72.9) 10 (14.3) 41 (58.6) 6 (8.5) 10 (14.3) 48 (68.7) 16 (22.9) 6 (8.6) 6 (8.6) 9 (12.9) 2 (2.9) 36 (51.4) 58 (82.9 15 (21.4) 43 (61.4) 12 (17.1) 7 (10.0) 3 (4.3) 2 (2.9) 10 (14.3)
Death N=50 (%) 47.1±3.2 29/21 (58.0/42.0) 79.01±8.4 19 (38.0) 22 (44.0) 43 (86.0) 28 (56.0) 22 (44.0) 34 (68.0) 39 (78.0) 10 (20.0) 29 (58.0) 18 (36.0) 7 (14.0) 46 (92.0) 14 (28.0) 18 (36.0) 8 (16.0) 19 (38.0) 7 (14.0) 31 (62.0) 35 (70.0) 5 (10.0) 30 (60.0) 15 (30.0) 10 (20.0) 2 (4.0) 3 (6.0) 24 (48.0)
Univariate OR1 (IC2 95%) 0.68 (0.30-1.53) 0.58 (0.26-1.29) 1.05 (0.47-2.32) 1.02 (0.32-3.28) 1.20 (0.54-2.66) 1.18 (0.53-2.63) 1.42 (0.62-3.26) 1.32 (0.52-3.38) 1.50 (0.52-4.35) 0.98 (0.44-2.18) 6.00 (1.99-18.92) 0.98 (0.31-3.08) 5.27 (1.55-19.67) 1.31 (0.53-3.27) 6.00 (1.99-18-92) 2.03 (0.58-7.21) 4.15 (1.55-11.35) 5.53 (0.98-40.62) 1.54 (0.69-3.45) 0.48 (0.19-1.25) 0.41 (0.12-1.32) 0.94 (0.42-2.12) 2.07 (0.80-5.39) 2.25 (0.71-7.23) 0.93 (0.10-7.21) 2.17 (0.28-19.44) 5.36 (2.13-13.77)
Odds 459 rati ; 2Confidence interval; 3P value; 4; 5Ascitic fluid, cavity abscess, wound secretion, ocular secretion, liquor. *P statistically significant
P3 0.09 0.40
