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Hindawi Publishing Corporation
Canadian Journal of Infectious Diseases and Medical Microbiology
Volume 2016, Article ID 1321487, 9 pages
http://dx.doi.org/10.1155/2016/1321487
Research Article
Evaluation of Risk Factors for Antibiotic
Resistance in Patients with Nosocomial Infections Caused by
Pseudomonas aeruginosa
Meliha Cagla Sonmezer,1 Gunay Ertem,1 Fatma Sebnem Erdinc,1 Esra Kaya Kilic,1
Necla Tulek,1 Ali Adiloglu,2 and Cigdem Hatipoglu1
1
Department of Clinic of Infectious Diseases and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey
Department of Microbiology and Clinical Microbiology, Ankara Training and Research Hospital, Ankara, Turkey
2
Correspondence should be addressed to Meliha Cagla Sonmezer; caglasonmezer@hotmail.com
Received 19 April 2016; Revised 3 July 2016; Accepted 25 July 2016
Academic Editor: Jorge Garbino
Copyright © 2016 Meliha Cagla Sonmezer et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background. Pseudomonas aeruginosa (P. aeruginosa) is resistant to various antibiotics and can cause serious nosocomial infections
with high morbidity and mortality. In this clinical study, we investigated the risk factors in patients who were diagnosed with P.
aeruginosa-related nosocomial infection. Methods. A retrospective case control study including patients with P. aeruginosa-related
nosocomial infection. Patients who were resistant to any of the six antibiotics (imipenem, meropenem, piperacillin-tazobactam,
ciprofloxacin, amikacin, and ceftazidime) constituted the study group. Results. One hundred and twenty isolates were isolated.
Various risk factors were detected for each antibiotic in the univariate analysis. In the multivariate analysis, previous cefazolin use
was found as an independent risk factor for the development of imipenem resistance (OR = 3.33; CI 95% [1.11–10.0]; ? = 0.03),
whereas previous cerebrovascular attack (OR = 3.57; CI 95% [1.31–9.76]; ? = 0.01) and previous meropenem use (OR = 4.13; CI 95%
[1.21–14.07]; ? = 0.02) were independent factors for the development of meropenem resistance. For the development of resistance
to ciprofloxacin, hospitalization in the neurology intensive care unit (OR = 4.24; CI 95% [1.5–11.98]; ? = 0.006) and mechanical
ventilator application (OR = 11.7; CI 95% [2.24–61.45]; ? = 0.004) were independent risk factors. Conclusion. The meticulous
application of contact measures can decrease the rate of nosocomial infections.
1. Introduction
Healthcare associated infections (HAIs) (or nosocomial
infections) are the worldwide public health problem causing
morbidity and mortality especially in the developing countries. Furthermore, if the causative organism has developed
resistance to a number of antimicrobial agents management
of the issue gets harder [1]. The developing countries have
taken the commendable strategies of introducing laws to
control of HAIs [2].
Intensive care units (ICUs) are units where healthcare
infections seen more often because of commonly critically
ill patients and invasive interventions used in these units. In
ICUs antimicrobial resistance rates are increasing because of
various reasons such as broad spectrum and/or inappropriate
antimicrobial usage and prolonged length of stay in hospital.
As a result, it increases healthcare infection rates caused
by multidrug resistant microorganisms. These infections
prolong hospitalization, require more extensive diagnostics
and treatment, and are associated with additional costs [3–5].
Device-associated healthcare associated infections (DAHAI) are defined by the Centers for Disease Control and
Prevention’s (CDC’s) National Healthcare Safety Network
(NHSN) as infections acquired in a hospital by a patient who
was admitted for a reason other than that infection [6, 7].
Worldwide view of HAIs in ICUs can be evaluated by
comparing studies containing developing and developed
several countries [8–10]. According to the KISS data of
Germany between 2005 and 2009, P. aeruginosa was causative
agent in 17.7% of ventilator associated pneumonia and 14.2%
of urinary catheter associated urinary tract infections of all
ICU infections [8]. According to the INICC data of Iran
2
Canadian Journal of Infectious Diseases and Medical Microbiology
between 2011 and 2012, P. aeruginosa was causative agent
in 19% of ventilator associated pneumonia, 5% of urinary
catheter associated urinary tract infections, 2% of blood
stream infections, and 7% of surgical site infections of all ICU
infections [9].
The infection rates for nosocomial infections and their
pathogens differ greatly between different types of ICU corresponding to the different risk structure of the patients. Different studies have been conducted to highlight the incidence
and importance of hospital acquired infections in ICUs, to
contribute to empirical treatment methods by determining
the causes of common hospital infections and antibiotic
resistance rates, to minimize the emergence of resistant
microorganisms by preventing unnecessary antibiotic use,
and to emphasize the need for protective measures against
risk factors that favor hospital infections [5–8].
P. aeruginosa is an important pathogen, especially in
immunocompromised patients. Besides, P. aeruginosa causes
infections with high morbidity and mortality in intensive care
units (ICUs). P. aeruginosa related infections are frequently
life threatening and often difficult to treat due to the intrinsic
resistance to many antimicrobial agents. Moreover, the resistance to antipseudomonal agents has become an increasing
problem in recent years [11–14].
The present study aims to determine the risk factors
for the emergence of P. aeruginosa infections those are
resistant to imipenem, meropenem, piperacillin-tazobactam,
amikacin, ceftazidime, or ciprofloxacin and compare the risk
factors between isolates that are resistant and sensitive to each
antibiotic separately. Furthermore, it aims to guide clinicians
regarding treatment and infection control by revealing the
relationship between antibiotic resistance and risk factors.
2. Material and Methods
2.1. Hospital Settings and Study Population. A retrospective
case-control study was conducted at Ankara Training and
Research Hospital in Turkey between January 2008 and July
2011. The hospital is a 670-bed referral and tertiary care
hospital. The hospital contains medical and surgical ICUs.
Neurology, neurosurgery, and anesthesia-reanimation ICUs
with 31 total bed capacity were included in the study.
2.2. Study Design and Data Collection. In the hospital, nosocomial infections in ICUs have been determined by prospective, laboratory-based and patient based active surveillance
since 2008. In the study, the relevant surveillance data has
been evaluated to determine the risk factors for resistant
P. aeruginosa related infections. Patients who underwent
inpatient treatment in these ICUs and were diagnosed as
having P. aeruginosa related infection 48 hours after being
hospitalized were included in the study. The patients with P.
aeruginosa resistant to selected antibiotics were defined as
case groups and the patients with P. aeruginosa sensitive to
the related antibiotic were defined as control groups.
A list of potential risk factors including the risk factors in
the hospital settings was formed consistent with the relevant
literature. The risk factors were as follows: gender, age, ICU
type, P. aeruginosa as a cause of multiple sites of infections,
being infected with other resistant microorganisms within 30
days before or concurrently with P. aeruginosa infection, existence of comorbid diseases, invasive procedures, antibiotic
use, and other drugs within 30 days before the isolation of
P. aeruginosa.
2.3. Microbiological Examination. All P. aeruginosa were
isolated from various clinical specimens in the hospital
microbiology laboratory by conventional biochemical methods. Recurrent isolates from the same patient were excluded
from the study. The identification and antibiotic susceptibilities of the isolates were made by VITEK II automated
system (Biome`rieux, France) and the results were interpreted
according to standards of Clinical and Laboratory Standards
Institute (CLSI) [15]. Intermediate susceptible isolates were
considered to be susceptible.
2.4. Definitions. Nosocomial infections were defined according to the criteria proposed by the Centers for Disease Control and Prevention (CDC) [6]. The patients with nosocomial
infection due to resistant (R) strains were compared with
those with susceptible (S) strains for the respective antimicrobial resistances, that is, imipenem (IMP-R), meropenem
(MEM-R), ceftazidime (CAZ-R), piperacillin-tazobactam
(TZP-R), ciprofloxacin (CIP-R), and amikacin (AK-R). The
risk factors in nosocomial infections for antimicrobial resistance to imipenem (IMP), meropenem (MEM), piperacillintazobactam (TZP), ceftazidime (CAZ), ciprofloxacin (CIP),
and amikacin (AK) were evaluated. After the hospitalization
of the patients, antibiotics that were taken 30 days before
isolation of P. aeruginosa and used for 48 hours and longer
were defined as previous antibiotic use. The elapsed time
between the admission to ICU and isolation of P. aeruginosa
was defined as the “risk period.”
2.5. Statistical Analysis. The SPSS 15.0 program was used for
statistical analysis. The Mann–Whitney ? test was used to
compare two independent groups. The Chi-square test was
used to analyze the categorical variables. In addition, the multiple logistic regression analysis was performed to determine
independent risk factors that were influential on being resistant to different antibiotics. Variables included in the model
were determined by using univariate statistical methods in
the multivariate analysis. Variables with a significance level
of ? < 0.05 were compared with multiple logistic regression analysis. Multiple logistic regression analysis results were summarized with odds ratios, 95% confidence interval, and ? values. In the presentation of demographic data as descriptive statistics, rates and frequency were given in qualitative variables, whereas medium (minimum-maximum) and/or mean ± standard deviation were given in quantitative variables. ? < 0.05 was regarded as significant. 3. Results 3.1. Demographic and Clinical Features. One hundred twenty isolates that were isolated from 120 patients and met the inclusion criteria were included in the study. Thirty-four (28.3%) patients were in neurosurgery ICU, 30 (25%) patients Canadian Journal of Infectious Diseases and Medical Microbiology were in Neurology ICU, and 56 (46.6%) patients were in anesthesia-reanimation ICU. During the study, 85 (70.8%) patients have been died. The hospitalization period was 4– 413 days in ICU; the period until P. aeruginosa isolation was 3–292 days. The distribution of P. aeruginosa related infection types was as follows: ventilator associated pneumonia in 61 patients (50.8%), urinary system infection in 39 patients (32.5%), wound infection in 13 patients (10.8%), bloodstream infection in five patients (4.1%), and catheter infection in two patients (1.6%). Among the patients diagnosed with P. aeruginosa related infection, 37 (30.8) patients were transferred from another ICU of the hospital and 15 patients (12.5%) were transferred from another hospital. One hundred five (87.5%) patients used an antimicrobial within 30 days before P. aeruginosa isolation. The most frequently used antibiotics were carbapenem (? = 73, 60.8%) and meropenem (? = 67, 90.1%). Demographic and clinical characteristics of the study population are described in Table 1. The majority of the isolates were resistant to imipenem (45.8%), and then to meropenem and aztreonam (each with 43.3%). The isolates were mostly sensitive to colistin (100%), followed by tobramycin (80%) and amikacin (78.3%). Multiple antibiotic resistance was observed in 37 isolates (31.6%). 3.2. Risk Factors Associated with Antimicrobial Resistance. Various risk factors were detected for each antibiotic between sensitive and resistant P. aeruginosa isolates in the univariate analysis. The factors associated with antimicrobial resistance to imipenem, meropenem, and piperacillin-tazobactam are shown in Table 2 and ceftazidime, ciprofloxacin, and amikacin are shown in Table 3. For the patients with IMP-R, MEM-R, TZP-R, CAZ-R, CIP-R, and AK-R P. aeruginosa, the common risk factors were as follows: infection with another microorganism prior to the isolation, an ICU stay > 60 days,
total parenteral nutrition usage as an invasive procedures,
comorbid cerebrovascular disease, history of cerebrovascular attack, and antimicrobial use (especially meropenem)
within 30 days before the isolation were performed using
variables that were significantly associated with the respective
antimicrobial resistance in univariate analyses (? < 0.05) and the identified independent risk factors are shown in Table 4. According to the analysis, independent risk factors were as follows: for imipenem resistance, previous cefazolin use; for meropenem resistance, history of cerebrovascular attack and previous meropenem use; for amikacin, stay in the ICU > 60 days. The independent risk factor associated
with resistance to ciprofloxacin, piperacillin-tazobactam, and
ceftazidime was history of stay in NR-ICU in multivariate
logistic regression analyses.
4. Discussion
The present study is significant as it is a comprehensive study
that investigates the risk factors in resistant P. aeruginosa
infections in ICUs. Previous studies on carbapenem-resistant
P. aeruginosa (CR-Pa) infections have shown that hospitalization in ICU is a major risk factor [16–18]. In the current study,
all patients were selected from ICUs. Since the mean ICU
stay was 112.7 ± 87.8 days, staying in the ICU > 60 days was
3
Table 1: Demographic and clinical characteristics of patients with P.
aeruginosa related nosocomial infection.
Characteristics
Age; years (mean ± SD)
>60 years old
Male/female
Intensive care unit (ICU)
a
NR-ICU
b
AR-ICU
c
NRS-ICU
Stay at ICU (mean ± SD)
Intensive care unit stay > 60 days
Polymicrobial infection
Multiple isolation of P. aeruginosa
Time at risk (mean ± SD)
APACHE II score (mean ± SD) (at time of
isolation)
Invasive procedures and comorbid disease
Mechanic ventilation
Enteral nutrition
Total parenteral nutrition
Thoracotomy tube
Urinary catheterization
Central venous catheterization
History of cerebrovascular disease
History of cardiovascular disease
History of surgical operation
History of chronic renal disease
History of malignancy
Prior antibiotic use
Carbapenems
Meropenem
Piperacillin-tazobactam
Amikacin
Teicoplanin
Cefazolin
Ceftriaxone
Ciprofloxacin
Ceftazidime
Number of patients
(%)
(?: 120)
58.4 ± 19.2
70–58.3
62–50.8/58–49.2
34–28.3
56–46.6
34–28.3
112.7 ± 87.8
82–68.3
90–75
50–41.7
55.4 ± 52.4
23.6 ± 4.14
96–80
105–87.5
87–72.5
17–14.2
119–99.2
108–90
78–65
98–81.7
40–33.3
32–26.7
69–57.5
73–60.8
67–90.1
50–41.7
44–36.7
38–31.7
29–24.2
22–18.3
10–8.3
9–7.5
a
NR-ICU: neurology intensive care unit, b AR-ICU: anesthesia-reanimation
intensive care unit, and c NRS-ICU: neurosurgery intensive care unit.
evaluated as a risk factor. ICU stay > 60 days was significantly
higher in patients with MEM-RPa or with IMP-RPa when
compared to patients with MEM-SPa or IMP-SPa. There was
no significant correlation between carbapenem resistance
and type of ICU. The univariate analysis showed a significant
correlation between multiple isolation of P. aeruginosa in the
same patient (recurrent infection) as well as polymicrobial
infection and imipenem or meropenem resistance. However
4
Canadian Journal of Infectious Diseases and Medical Microbiology
Table 2: Univariate analysis of risk factors for antimicrobial resistance in nosocomial infections due to P. aeruginosa (IMP, MEM, and TZP).
1
Risk factors
>60 years old
Sex (male)
Intensive care unit
type
a
NR-ICU
b
AR-ICU
c
NRS-ICU
Intensive care unit
stay > 60 days
Polymicrobial
infection
Multiple isolation of
P. aeruginosa
Time at risk > 30 days
APACHE II score
(mean ± SD)
(at time of isolation)
Mechanic ventilation
Enteral nutrition
Total parenteral
nutrition
Thoracotomy tube
History of
cerebrovascular
disease
History of
cardiovascular disease
History of surgical
operation
Prior receipt of
cefazolin
Prior receipt of
ceftazidime
Prior receipt of
meropenem
Prior receipt of
amikacin
Prior receipt of
piperacillintazobactam
Prior receipt of
ciprofloxacin
Prior receipt of
teicoplanin
IMP-* R
(? = 56)
(?-%)
IMP-** S
(? = 64)
(?-%)
31–55.4
28–50
2
3
?
MEM-R
(? = 52)
(?-%)
MEM-S
(? = 68)
(?-%)
?
TZP-R
(? = 44)
(?-%)
TZP-S
(? = 76)
(?-%)
?
39–60.9
33–51.6
0.536
0.864
27–51.9
24–46.2
43–63.2
35–51.5
0.213
0.564
26–59.1
20–45.5
44–57.9
41–53.9
0.898
0.370
14–25
25–44.6
17–30.4
16–25
32–50
16–25
0.782
16–30.8
20–38.5
16–30.8
14–20.6
37–54.4
17–25
0.206
20–45.5
17–38.6
7–15.9
10–13.2
40–52.6
26–34.2
<0.001 47–83.9 35–54.7 0.001 46–88.5 36–52.9 <0.001 34–77.3 48–63.2 0.109 49–87.5 41–64.1 0.003 46–88.5 44–64.7 0.003 38–86.4 52–68.4 0.029 34–60.7 16–25 <0.001 32–61.5 18–26.5 <0.001 19–43.2 31–40.8 0.798 43–76.8 36–56.2 0.018 40–76.9 39–57.4 0.024 32–72.7 47–61.8 0.226 24.1 ± 5.8 23.1 ± 6.6 0.435 24 ± 5.6 22.2 ± 4.6 0.739 24.6 ± 6.3 23 ± 6.1 0.164 48–85.7 53–94.6 48–75 52–81.3 0.143 0.027 44–84.6 48–92.3 42–72.5 57–83.8 0.269 0.164 38–86.4 40–90.9 58–76.3 65–85.5 0.185 0.390 47–83.9 40–62.5 0.009 43–82.7 44–64.7 0.029 38–86.4 49–64.5 0.010 9–16.1 8–12.5 0.576 7–13.5 10–14.7 0.846 7–15.9 10–13.2 0.677 40–71.4 38–59.4 0.167 42–80.8 36–52.9 0.002 30–68.2 48–63.2 0.578 49–87.5 49–76.6 0.122 47–90.4 51–75 0.031 39–88.6 59–77.6 0.133 21–37.5 19–29.7 0.365 18–34.6 22–32.4 0.794 9–20.5 31–40.8 0.023 19–33.9 10–15.6 0.019 14–26.9 15–22.1 0.537 7–15.9 22–28.9 0.108 8–14.3 1–1.6 0.008 7–13.5 2–2.9 0.030 2–4.5 7–9.2 0.350 42–75 25–39.1 <0.001 42–80.8 25–36.8 <0.001 31–70.5 36–47.4 0.014 28–50 16–25 0.005 26–50 18–26.5 0.008 18–40.9 26–34.2 0.463 26–46.4 24–37.5 0.322 25–48.1 25–36.8 0.213 24–54.5 26–34.2 0.029 4–7.1 6–9.4 0.659 7–13.5 3–4.4 0.076 7–15.9 3–3.9 0.022 25–44.6 13–20.3 0.004 20–38.5 18–26.5 0.162 16–36.4 18–23.7 0.137 a NR-ICU: neurology intensive care unit, b AR-ICU: anesthesia-reanimation intensive care unit, and c NRS-ICU: neurosurgery intensive care unit. IMP: imipenem, 2 MEM: meropenem, and 3 TZP: piperacillin-tazobactam. * R: resistant and ** S: sensitive. 1 in multivariate analyses these variables were not detected as independent risk factors. Studies on carbapenem-resistant Pa infections have not frequently focused on these two risk factors. The means of time at risk (until the isolation of P. aeruginosa) were higher in both the imipenem and meropenem-resistant group. Longer risk periods increase the ratios of infection with resistant microorganisms. The high rate of infection history with another microorganism further Canadian Journal of Infectious Diseases and Medical Microbiology 5 Table 3: Univariate analysis of risk factors for antimicrobial resistance in nosocomial infections due to P. aeruginosa (CIP, AK, and CAZ). 1 Risk factors >60 years old
Sex (male)
I …
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