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Kang, Moon, Mun, Lee, Lee, Lee, and Lee: Antimicrobial Susceptibility Trends and Risk Factors for Antimicrobial Resistance in Pseudomonas aeruginosa Bacteremia: 12-Year Experience in a Tertiary Hospital in Korea
Original Article
Infectious Diseases, Microbiology & Parasitology

Journal of Korean Medical Science 2021; 36(43): e273.

Published online: 23 September 2021

DOI: https://doi.org/10.3346/jkms.2021.36.e273

Antimicrobial Susceptibility Trends and Risk Factors for Antimicrobial Resistance in Pseudomonas aeruginosa Bacteremia: 12-Year Experience in a Tertiary Hospital in Korea

Jin Suk Kang1, Chisook Moon1, Seok Jun Mun1, Jeong Eun Lee2, Soon Ok Lee2, Shinwon Lee2, Sun Hee Lee2

1Division of Infectious Diseases, Department of Internal Medicine, Inje University Busan Paik Hospital, Inje University College of Medicine, Busan, Korea.

2Division of Infectious Diseases, Department of Internal Medicine, Pusan National University School of Medicine and Medical Research Institute, Pusan National University Hospital, Busan, Korea.

Address for Correspondence: Chisook Moon, MD, PhD. Division of Infectious Diseases, Department of Internal Medicine, Inje University Busan Paik Hospital, Inje University College of Medicine, 75 Bokji-ro, Busanjin-gu, Busan 47392, Republic of Korea. duomon@hanmail.net

Received 19 June 2021       Accepted 12 September 2021

© 2021 The Korean Academy of Medical Sciences.

The Korean Academy of Medical Sciences

https://creativecommons.org/licenses/by-nc/4.0/
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Infections caused by multidrug-resistant Pseudomonas aeruginosa (MDRPA) have been on the rise worldwide, and delayed active antimicrobial therapy is associated with high mortality. However, few studies have evaluated increases in P. aeruginosa infections with antimicrobial resistance and risk factors for such antimicrobial resistance in Korea. Here, we analyzed changes in antimicrobial susceptibility associated with P. aeruginosa bacteremia and identified risk factors of antimicrobial resistance.

Methods

The medical records of patients with P. aeruginosa bacteremia who were admitted to a tertiary hospital between January 2009 and October 2020 were retrospectively reviewed. Antibiotic resistance rates were compared among the time periods of 2009–2012, 2013–2016, and 2017–2020 and between the intensive care unit (ICU) and non-ICU setting. Empirical antimicrobial therapy was considered concordant, if the organism was susceptible to antibiotics in vitro, and discordant, if resistant.

Results

During the study period, 295 patients with P. aeruginosa bacteremia were identified. The hepatobiliary tract (26.8%) was the most common primary site of infection. The rates of carbapenem-resistant P. aeruginosa (CRPA), MDRPA, and extensively drug-resistant P. aeruginosa (XDRPA) were 24.7%, 35.9%, and 15.9%, respectively. XDRPA showed an increasing trend, and CRPA, MDRPA, and XDRPA were also gradually increasing in non-ICU setting. Previous exposure to fluoroquinolones and glycopeptides and urinary tract infection were independent risk factors associated with CRPA, MDRPA, and XDRPA. Previous exposure to carbapenems was an independent risk factor of CRPA. CRPA, MDRPA, and XDRPA were associated with discordant empirical antimicrobial therapy.

Conclusion

The identification of risk factors for antimicrobial resistance and analysis of antimicrobial susceptibility might be important for concordant empirical antimicrobial therapy in patients with P. aeruginosa bacteremia.

Graphical Abstract

jkms-36-e273-abf001.jpg

Keywords: Pseudomonas aeruginosa, Bacteremia, Multidrug Resistance, Risk Factors

INTRODUCTION

Pseudomonas aeruginosa is an important pathogen in healthcare-associated infections, particularly P. aeruginosa bacteremia, which is associated with high rates of mortality and morbidity.12 The 30-day mortality rate associated with P. aeruginosa bacteremia ranges from 39% to 41%,34 and delayed active antimicrobial therapy can lead to worse outcomes.3567
In the United States, 6,700 cases in 2013 and 32,600 cases in 2017 of multidrug-resistant P. aeruginosa (MDRPA) infection were reported in hospitalized patients89; thus, increasing antibiotic resistance in P. aeruginosa among hospitalized patients is a major public health issue.10 In particular, infections caused by MDRPA or extensively drug-resistant P. aeruginosa (XDRPA) are a therapeutic challenge in terms of early active antibiotic use because effective antibiotics are limited.611 Thus, an analysis of antimicrobial susceptibility and the identification of risk factors for antimicrobial resistance might be important for early treatment initiation with active antibiotics in patients with P. aeruginosa bacteremia.
The report of the Korea Antimicrobial Resistance Monitoring System (KARMS) showed that the rate of carbapenem-resistant P. aeruginosa (CRPA) increased from 2010 (24.3%) to 2015 (41.0%) in sputum, urine, and wound specimens from general hospitals, and the rate of MDRPA was 18.2% in 2016.12 The report of the Korea Global Antimicrobial Resistance Surveillance System (Kor-GLASS) showed that the rates of MDRPA and XDRPA were 14.7% and 10.7% in blood specimens from general hospitals.13 However, KARMS results included several types of specimens in which infection and colonization were indistinguishable, and the data collection methods of Kor-GLASS and KARMS are different, and thus, it is difficult to evaluate the increase in MDRPA infections. Several studies have assessed risk factors for antibiotic-resistant P. aeruginosa in specific populations from different countries.14151617 In Korea, Lee et al.18 reported that carbapenem exposure is a risk factor for P. aeruginosa resistant only to carbapenems, and Joo et al.19 reported that ceftazidime, piperacillin, and imipenem resistance in P. aeruginosa is associated with indwelling urinary catheter and prior exposure to fluoroquinolone based on 2006–2009 data. However, there is no recent clinical study of the risk factors for antibiotic-resistant P. aeruginosa or MDRPA infections in Korea. Therefore, in this study, we aimed to analyze the antimicrobial susceptibility trends in P. aeruginosa bacteremia in a tertiary hospital and evaluate the risk factors associated with antibiotic-resistant P. aeruginosa and their outcomes.

METHODS

Study population and data collection

We retrospectively reviewed the medical records of patients with P. aeruginosa bacteremia admitted to Inje University Busan Paik Hospital, Busan, South Korea, from January 2009 to October 2020. The study hospital is a university-affiliated and tertiary hospital with 850 beds, four different intensive care units (ICUs) with 56 beds, and one hematopoietic stem cell transplantation unit. Patients with P. aeruginosa bacteremia who had a confirmed diagnosis based on microbiological laboratory results were included in the study. Patients less than 18 years of age were excluded from the analysis. Demographic and clinical characteristics, such as age; sex; underlying diseases; Charlson comorbidity index score; stay in the ICU; hospital stay or healthcare within 30 days before P. aeruginosa bacteremia; prior surgery within 90 days before P. aeruginosa bacteremia; previous exposure (≥ 1 day) to any antimicrobials within 90 days before P. aeruginosa bacteremia; colonization with multidrug-resistant organisms (MDROs), including carbapenem-resistant Enterobacteriaceae (CRE), extended-spectrum β-lactamase-producing bacteria, multidrug-resistant Acinetobacter baumannii, methicillin-resistant Staphylococcus aureus, or vancomycin-resistant Enterococcus; use of medical devices (central venous catheter, ventilator, or indwelling urinary catheter) before bacteremia; polymicrobial infection; primary site of infection; antimicrobial therapy; length of hospital stay; presentation with septic shock; and 30-day mortality were obtained from medical records.
Our first aim was to evaluate whether clinically important CRPA, MDRPA, and XDRPA bacteremia were increased in a tertiary hospital. We presented antimicrobial susceptibility results for the period from 2009 to 2020, and since it was relatively small annual data from a single center, we compared the results by dividing them into three groups at 4-year intervals to analyze the trend in antibiotic susceptibility (i.e., 2009–2012, 2013–2016, and 2017–2020). Moreover, we compared the antibiotic resistance rates in P. aeruginosa bacteremia in ICU and non-ICU settings. Our second aim was to evaluate risk factors associated with antibiotic resistance in P. aeruginosa bacteremia and their outcomes. In addition, to identify the difference in mortality according to the antibiotic-resistant strain, we performed an additional evaluation of patients who did not show polymicrobial bacteremia, received active antimicrobial therapy during their hospital stay, and were hospitalized for more than 5 days after the onset of bacteremia.

Microbiological methods

Blood culture was performed using the automated BACTEC FX system (Becton Dickinson, Sparks, MD, USA) or a BacT/Alert 3D system (bioMérieux, Marcy l'Etoile, France). Bacterial identification and antimicrobial susceptibility tests were performed using the Vitek II automated system (bioMérieux). The results of the antimicrobial susceptibility test were interpreted based on the Clinical and Laboratory Standards Institute (CLSI) guidelines, and all the results were re-evaluated based on the revised CLSI guidelines.20 Intermediate susceptibility was defined as being non-susceptible.10

Definitions

P. aeruginosa bacteremia was defined as isolation of the organism from at least one bottle of blood culture grown using samples from patients with symptoms and signs of infection. In the case of patients with recurrent P. aeruginosa bacteremia during hospitalization, only the first P. aeruginosa bacteremia episode for each patient was included in the study. Polymicrobial bacteremia was defined as either the growth of one or more different microorganisms from blood culture in which P. aeruginosa was identified or the growth of species other than P. aeruginosa in two or more separate blood cultures within the same case.21 Colonization with MDROs was defined as a positive rectal swab, nasal swab, urine, sputum, and other clinical specimens, and polymicrobial bacteremia and other infections were excluded. Healthcare-associated infection was defined as confirmed P. aeruginosa bacteremia in patients who had been hospitalized for more than 48 hours or in patients with a history of hospitalization or healthcare such as outpatient chemotherapy or dialysis within 1 month.2223 The primary site of infection was defined by documented or presumed clinical signs, laboratory findings, and radiologic findings according to National Healthcare Safety Network surveillance definitions.24 Gastrointestinal system infections were classified as gastrointestinal tract and hepatobiliary tract, and respiratory tract infections included both non-ventilator-associated pneumonia and ventilator-associated pneumonia. Neutropenia was defined as an absolute neutrophil count of less than 500 cells/mm3. Septic shock was defined as sepsis with persisting hypotension and requiring vasopressor therapy needed to maintain mean arterial pressure at ≥ 65 mmHg despite adequate fluid resuscitation.25
Antimicrobial categories were classified as aminoglycosides (amikacin, gentamicin), antipseudomonal carbapenems (imipenem, meropenem), antipseudomonal cephalosporins (ceftazidime, cefepime), antipseudomonal penicillin with β-lactamase inhibitors (piperacillin-tazobactam, ticarcillin-clavulanic acid), fluoroquinolones (ciprofloxacin), monobactams (aztreonam), and polymyxins (colistin). Antimicrobial-resistant P. aeruginosa was categorized as follows: 1) CRPA when resistance or intermediate resistance was confirmed with one or more carbapenems having antipseudomonal activity (e.g., imipenem or meropenem) as per CLSI guidelines520; 2) MDRPA when the organism was not susceptible to one or more agents in at least three antimicrobial categories; 3) XDRPA when the organism was not susceptible to at least one agent in all but two or fewer antimicrobial categories; and 4) pandrug-resistant P. aeruginosa (PDRPA) when the organism was not susceptible to all antimicrobial categories.26
Active antimicrobial therapy was defined as antibiotics demonstrated to be active in vitro against blood isolates of P. aeruginosa during the treatment period.5 Concordant empirical antimicrobial therapy was defined as active antimicrobial therapy administered less than or equal to 48 hours after obtaining blood culture samples. Discordant empirical antimicrobial therapy was defined as active antimicrobial therapy not administered within 48 hours after obtaining blood culture samples.2728

Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA). All continuous variables are summarized as medians and interquartile ranges (IQRs), and categorical variables are described using frequencies and percentiles. Categorical variables were compared using Pearson's χ2 tests or Fisher's exact tests, whereas noncategorical variables were tested using Mann-Whitney tests. Multivariable analysis was performed for variables that had a P value less than 0.05 in univariable analysis. Risk factors for antibiotic resistance in P. aeruginosa bacteremia were evaluated using logistic regression analysis. The risk factors of 30-day mortality were analyzed using cox proportional hazard regression. Results with P values less than 0.05 were considered statistically significant.

Ethics statement

The study protocol was approved by the institutional review board (IRB) of Inje University Busan Paik Hospital (IRB number: 2020-11-003), and the need for informed consent was waived owing to the retrospective nature of the study.

RESULTS

Antimicrobial susceptibility trends in P. aeruginosa bacteremia

During the study period, 295 patients with P. aeruginosa bacteremia were identified. Antimicrobial susceptibility results are shown in Figs. 1 and 2. Among isolates, 213 (72.2%) showed non-susceptibility to one or more antibiotics. The susceptibility rate of P. aeruginosa to only colistin and amikacin was more than 90% (Table 1). The susceptibility rates for meropenem (P = 0.048) and ciprofloxacin (P = 0.041) were significantly decreased during 2013–2016 compared with those during 2009–2012 (Fig. 2).
Fig. 1

Annual antimicrobial susceptibility trends in P. aeruginosa bacteremia.

jkms-36-e273-g001
Fig. 2

Results of antimicrobial susceptibility in P. aeruginosa bacteremia for 2009–2012, 2013–2016, and 2017–2020. The P value was a comparison between susceptible and non-susceptible.

NA = not applicable, S = susceptible, I = intermediate, R = resistant.
jkms-36-e273-g002
Table 1

Antimicrobial susceptibility rates in P. aeruginosa bacteremia

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Characteristics Total (n = 295) 2009–2012 (n = 87) 2013–2016 (n = 94) 2017–2020 (n = 114) P
Amikacin 269 (91.2) 77 (88.5) 89 (94.7) 103 (90.4) 0.316
Gentamicin 246 (83.4) 75 (86.2) 82 (87.2) 89 (78.1) 0.147
Imipenem 220 (74.6) 71 (81.6) 65 (69.1) 84 (73.7) 0.151
Meropenem 222 (75.3) 72 (82.8) 66 (70.2) 84 (73.7) 0.131
Cefepime 228 (77.3) 68 (78.2) 76 (80.9) 85 (74.6) 0.551
Ceftazidime 226 (76.6) 67 (77.0) 73 (77.7) 86 (75.4) 0.926
Piperacillin-tazobactam 193 (65.4) 57 (65.5) 64 (68.1) 72 (63.2) 0.758
Ticarcillin-clavulanate 99 (33.6) 23 (26.4) 37 (39.4) 39 (34.2) 0.181
Ciprofloxacin 211 (71.5) 70 (80.5) 63 (67.0) 78 (68.4) 0.087
Aztreonam 167 (56.6) 57 (65.5) 52 (55.3) 58 (50.8) 0.111
Colistin 294 (99.7) 87 (100) 94 (100) 113 (99.1) 1.000
Values are presented as number of patients (%).
The rates of CRPA, MDRPA, and XDRPA were 24.7% (73/295), 35.9% (106/295), and 15.9% (47/295), respectively. Among MDRPA and XDRPA, CRPA was identified as 65 (61.3%) and 43 (91.5%) cases, respectively. PDRPA was not identified. There was one colistin-resistant strain, but it was susceptible to anti-pseudomonal cephalosporines and carbapenems, and further evaluation was not performed. The rates of CRPA, MDRPA, and XDRPA in the ICU were higher than those in non-ICU settings (CRPA: 50% vs. 19.8%, P < 0.001; MDRPA: 50% vs. 33.2%, P = 0.026; and XDRPA: 25% vs. 14.2%, P = 0.061). The XDRPA rate was significantly increased during 2017–2020 compared with that during 2009-2012 (P = 0.023; Fig. 3). During 2017–2020, the antibiotic resistance rates in the ICU decreased compared to 2013–2016, whereas those in the non-ICU setting gradually increased over time.
Fig. 3

Antimicrobial resistance trends in P. aeruginosa bacteremia. (A) Antimicrobial resistance rates in P. aeruginosa bacteremia. (B) Comparison of antibiotic resistance rates of the ICUs vs. the non-ICUs setting.

CRPA = carbapenem-resistant P. aeruginosa, MDRPA = multidrug-resistant P. aeruginosa, XDRPA = extensively drug-resistant P. aeruginosa, ICU = intensive care unit.
*Comparison between 2009–2013 and 2017–2020; **Comparison of the three groups.
jkms-36-e273-g003

Clinical characteristics of patients with P. aeruginosa bacteremia

The median age of 295 patients was 68 years (IQR, 60–76 years; Table 2). Among all patients, 245 (83.1%) had healthcare-associated infection, 221 (74.9%) had a history of antibiotic exposure within 90 days, and 155 (52.5%) patients had solid cancer. Of the patients with solid cancer, 9 (5.8%) were being followed after remission, 31 (20%) were hospitalized for cancer diagnosis, and 76 (49.0%) were receiving chemotherapy. The hepatobiliary tract (26.8%) was the most common primary site of infection, followed by the respiratory (23.4%) and urinary (16.9%) tracts. Fifty (16.9%) patients had polymicrobial bacteremia, and the common primary site of infection were the hepatobiliary tract (16/50, 32.0%) and central venous catheter (10/50, 20.0%). Of 79 patients with hepato-biliary tract infection, 55 (69.6%) had undergone endoscopic retrograde cholangiopancreatography (37/79, 46.8%) or percutaneous drainage (18/79, 22.8%). Of 50 patients with urinary tract infection, 30 (60%) were catheter-associated urinary tract infections.
Table 2

Clinical characteristics, treatment, and outcomes in patients with P. aeruginosa bacteremia

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Characteristics Total (n = 295) CRPA (n = 73) Non-CRPA (n = 222) P MDRPA (n = 106) Non-MDRPA (n = 189) P XDRPA (n = 47) Non- XDRPA (n = 248) P
Age, yrs 68 (60–76) 72 (60–77) 68 (60–75) 0.078 72 (61–77) 67 (57–75) 0.041 73 (65–79) 68 (58–75) 0.005
Male sex 197 (66.8) 53 (72.6) 144 (64.9) 0.223 73 (68.9) 124 (65.6) 0.568 34 (72.3) 163 (65.7) 0.377
Underlying diseases or conditions
Cardiovascular disease 30 (10.2) 10 (13.7) 20 (9.0) 0.250 10 (9.4) 20 (10.6) 0.754 6 (12.8) 24 (9.7) 0.597
Cerebrovascular accident 40 (13.6) 19 (26.0) 21 (9.5) < 0.001 21 (19.8) 19 (10.1) 0.019 13 (27.7) 27 (10.9) 0.002
Chronic kidney disease 24 (8.1) 6 (8.2) 18 (8.1) 0.976 8 (7.5) 16 (8.5) 0.782 6 (12.8) 18 (7.3) 0.240
COPD or chronic lung disease 7 (2.4) 2 (2.7) 5 (2.3) 0.812 2 (1.9) 5 (2.6) 1.000 2 (4.3) 5 (2.0) 0.309
Dementia 14 (4.7) 3 (4.1) 11 (5.0) 1.000 5 (4.7) 9 (4.8) 0.986 4 (8.5) 10 (4.0) 0.250
Diabetes 84 (28.5) 23 (31.5) 61 (27.5) 0.508 35 (33.0) 49 (25.9) 0.195 19 (40.4) 65 (26.2) 0.048
Heart failure 14 (4.7) 2 (2.7) 12 (5.4) 0.353 4 (3.8) 10 (5.3) 0.556 2 (4.3) 12 (4.8) 1.000
Hypertension 112 (38.0) 30 (41.1) 82 (36.9) 0.525 43 (40.6) 69 (36.5) 0.491 23 (48.9) 89 (35.9) 0.091
Liver disease 12 (4.1) 2 (2.7) 10 (4.5) 0.737 4 (3.8) 8 (4.2) 1.000 0 (0.0) 12 (4.8) 0.225
Solid cancer 155 (52.5) 30 (41.1) 125 (56.3) 0.024 47 (44.3) 108 (57.1) 0.035 15 (31.9) 140 (56.5) 0.002
Hematologic malignancy 26 (8.8) 10 (13.7) 16 (7.2) 0.090 10 (9.4) 16 (8.5) 0.778 6 (12.8) 20 (8.1) 0.297
Immunosuppressive therapy 102 (34.6) 20 (27.4) 82 (36.9) 0.137 24 (22.6) 78 (41.3) 0.001 12 (25.5) 90 (36.3) 0.155
Neutropenia 54 (18.3) 10 (13.7) 44 (19.8) 0.241 13 (12.3) 41 (21.7) 0.045 7 (14.9) 47 (19.0) 0.509
Transplantation 7 (2.4) 3 (4.1) 4 (1.8) 0.261 3 (2.8) 4 (2.1) 0.705 2 (4.3) 5 (2.0) 0.309
CCI score 5 (4–8) 5 (3–7) 5 (4–8) 0.181 5 (3–7) 5 (4–8) 0.118 5 (3–7) 5 (4–8) 0.167
CCI score ≥ 5 170 (57.6) 37 (50.7) 133 (59.9) 0.166 56 (52.8) 114 (60.3) 0.212 24 (51.1) 146 (58.9) 0.321
Healthcare-associated infection 245 (83.1) 67 (91.8) 178 (80.2) 0.022 93 (87.7) 152 (80.4) 0.108 42 (89.4) 203 (81.9) 0.209
Previous surgery within 90 days 66 (22.4) 18 (24.7) 48 (21.6) 0.589 21 (19.8) 45 (23.8) 0.429 10 (21.3) 56 (22.6) 0.844
Any antibiotic exposure within 90 days 221 (74.9) 61 (83.6) 160 (72.1) 0.049 87 (82.1) 134 (70.9) 0.034 38 (80.9) 183 (73.8) 0.306
Aminoglycosides 14 (4.7) 8 (11.0) 6 (2.7) 0.004 9 (8.5) 5 (2.6) 0.023 6 (12.8) 8 (3.2) 0.013
3rd/4th generation cephalosporins 152 (51.5) 44 (60.3) 108 (48.6) 0.085 64 (60.4) 88 (46.6) 0.023 26 (55.3) 126 (50.8) 0.570
Anti-pseudomonal penicillins 49 (16.6) 19 (26.0) 30 (13.5) 0.013 23 (21.7) 26 (13.8) 0.079 10 (21.3) 39 (15.7) 0.348
Carbapenems 42 (14.2) 30 (41.1) 12 (5.4) < 0.001 28 (26.4) 14 (7.4) < 0.001 15 (31.9) 27 (10.9) < 0.001
Fluoroquinolones 66 (22.4) 33 (45.2) 33 (14.9) < 0.001 36 (34.0) 30 (15.9) < 0.001 22 (46.8) 44 (17.7) < 0.001
Metronidazole 46 (15.6) 16 (21.9) 30 (13.5) 0.086 21 (19.8) 25 (13.2) 0.135 11 (23.4) 35 (14.1) 0.107
Clindamycin 13 (4.4) 8 (11.0) 5 (2.3) 0.004 7 (6.6) 6 (3.2) 0.236 5 (10.6) 8 (3.2) 0.039
Glycopeptides 40 (13.6) 27 (37.0) 13 (5.9) < 0.001 27 (25.5) 13 (6.9) < 0.001 14 (29.8) 26 (10.5) < 0.001
Linezolid 8 (2.7) 4 (5.5) 4 (1.8) 0.093 4 (3.8) 4 (2.1) 0.464 1 (2.1) 7 (2.8) 1.000
Tigecycline 7 (2.4) 5 (6.8) 2 (0.9) 0.004 5 (4.7) 2 (1.1) 0.102 1 (2.1) 6 (2.4) 1.000
Colistin 7 (2.4) 5 (6.8) 2 (0.9) 0.004 5 (4.7) 2 (1.1) 0.102 1 (2.1) 6 (2.4) 1.000
Colonization with MDROs
CRE 4 (1.4) 2 (2.7) 2 (0.9) 0.239 3 (2.8) 1 (0.5) 0.134 1 (2.1) 3 (1.2) 0.502
ESBL 27 (9.4) 10 (13.7) 17 (7.7) 0.120 10 (9.4) 17 (9.0) 0.900 4 (8.5) 23 (9.3) 1.000
MRAB 16 (5.4) 8 (11.0) 8 (3.6) 0.016 9 (8.5) 7 (3.7) 0.082 2 (4.3) 14 (5.6) 1.000
MRSA 5 (1.7) 1 (1.4) 4 (1.8) 1.000 1 (0.9) 4 (2.1) 0.658 0 (0) 5 (2.0) 1.000
VRE 19 (6.4) 13 (17.8) 6 (2.7) < 0.001 12 (11.3) 7 (3.7) 0.011 5 (10.6) 14 (5.6) 0.200
ICU stay 48 (16.3) 24 (32.9) 24 (10.8) < 0.001 24 (22.6) 24 (12.7) 0.026 12 (25.5) 36 (14.5) 0.061
Devices during time at risk
Central venous catheter 107 (36.3) 34 (46.6) 73 (32.9) 0.035 36 (34.0) 71 (37.6) 0.537 19 (40.4) 88 (35.5) 0.518
Ventilator 29 (9.8) 13 (17.8) 16 (7.2) 0.008 18 (17.0) 11 (5.8) 0.002 7 (14.9) 22 (8.9) 0.192
Indwelling urinary catheter 100 (33.9) 37 (50.7) 63 (28.4) < 0.001 51 (48.1) 49 (25.9) < 0.001 24 (51.1) 76 (30.6) 0.007
Shock on the first day of bacteremia 84 (28.5) 21 (28.8) 63 (28.4) 0.949 31 (29.2) 53 (28.0) 0.826 14 (29.8) 70 (28.2) 0.828
Primary site of infection
Hepato-biliary tract 79 (26.8) 19 (26.0) 60 (27.0) 0.867 42 (39.6) 37 (19.6) < 0.001 13 (27.7) 66 (26.6) 0.882
Gastrointestinal tract 19 (6.4) 3 (4.1) 16 (7.2) 0.424 4 (3.8) 15 (7.9) 0.162 2 (4.3) 17 (6.9) 0.748
Respiratory tract 69 (23.4) 13 (17.8) 56 (25.2) 0.194 18 (17.0) 51 (27.0) 0.051 10 (21.3) 59 (23.8) 0.709
Urinary tract 50 (16.9) 22 (30.1) 28 (12.6) 0.001 25 (23.6) 25 (13.2) 0.023 16 (34.0) 34 (13.7) < 0.001
Central venous catheter 39 (13.2) 11 (15.1) 28 (12.6) 0.591 10 (9.4) 29 (15.3) 0.150 4 (8.5) 35 (14.1) 0.357
Skin and soft tissue 12 (4.1) 2 (2.7) 10 (4.5) 0.508 3 (2.8) 9 (4.8) 0.547 1 (2.1) 11 (4.1) 0.698
Surgical site 2 (0.7) 0 (0) 2 (0.9) 1.000 0 (0.0) 2 (1.1) 0.538 0 (0.0) 2 (0.8) 1.000
Primary bloodstream 25 (8.5) 3 (4.1) 22 (9.9) 0.123 4 (3.8) 21 (11.1) 0.030 1 (2.1) 24 (9.7) 0.147
Polymicrobial bacteremia 50 (16.9) 11 (15.1) 39 (17.6) 0.622 15 (14.2) 35 (18.5) 0.337 5 (10.6) 45 (18.1) 0.209
Invasive drainage procedures 62 (21.0) 13 (17.8) 49 (22.1) 0.438 30 (28.3) 32 (16.9) 0.021 10 (21.3) 52 (21.0) 0.962
Active antimicrobial therapy 244 (82.7) 54 (74.0) 190 (85.6) 0.023 77 (72.6) 167 (88.4) 0.001 34 (72.3) 210 (84.7) 0.040
Concordant empirical antimicrobial therapy 181 (61.4) 25 (34.2) 156 (70.3) < 0.001 36 (34.0) 145 (76.7) < 0.001 15 (31.9) 166 (66.9) < 0.001
Single antibiotics 151 (51.2) 23 (31.5) 128 (57.7) 34 (32.1) 117 (61.9) 14 (29.8) 137 (55.2)
Combination antibiotics 30 (10.2) 2 (2.7) 28 (12.6) 2 (1.9) 28 (14.8) 1 (2.1) 29 (11.7)
Duration of active antibiotics, days 8 (1–15) 7 (0–14) 8 (2–14) 0.172 7 (0–14) 8 (2–15.5) 0.072 8 (0–15) 8 (2–14.8) 0.207
Length of hospital stay after bacteremia, days 10 (4–18) 12 (5–23) 10 (5–18) < 0.001 10 (5–17) 9 (4–18) 0.213 11 (5–22) 9 (4–17.5) 0.204
30-day mortality 80 (27.1) 22 (30.1) 58 (26.1) 0.504 28 (35.0) 52 (27.5) 0.839 14 (29.8) 66 (26.6) 0.654
Values are presented as median (interquartile range) or number (%).
CRPA = carbapenem-resistant P. aeruginosa, MDRPA = multidrug-resistant P. aeruginosa, XDRPA = extensively drug-resistant P. aeruginosa, COPD = chronic obstructive pulmonary disease, CCI = Charlson comorbidity index, MDRO = multidrug-resistant organism, CRE = carbapenem-resistant Enterobacteriaceae, ESBL = extended-spectrum beta-lactamase-producing bacteria, MRAB = multi-drug resistant Acinetobacter baumannii, MRSA = methicillin-resistant Staphylococcus aureus, VRE = vancomycin-resistant Enterococcus, ICU = intensive care unit.
A total of 244 (82.7%) patients received active antimicrobial therapy, and 181 (61.4%) patients received concordant empirical antimicrobial therapy (Table 2). Patients with septic shock received more carbapenem therapy as empirical antimicrobial therapy than patients without shock (28.6% vs. 15.6%, P = 0.011). CRPA, MDRPA, and XDRPA were significantly more frequent in patients who had underlying cerebrovascular accidents, those with an indwelling urinary catheter, and those exposed to aminoglycosides, carbapenems, fluoroquinolones, and glycopeptides. Moreover, CRPA, MDRPA, and XDRPA were significantly associated with discordant empirical antimicrobial therapy. Of XDRPA, 21.2% (10/47) were nursing hospital-associated infections.

Risk factors associated with antimicrobial resistance in P. aeruginosa bacteremia

In multivariable analysis, urinary tract infection and previous exposure to fluoroquinolones and glycopeptides were independent risk factors for CRPA, MDRPA, and XDRPA (Table 3). Risk factors for CRPA were the presence of an underlying cerebrovascular accident and previous exposure to carbapenems. Risk factors for MDRPA were the presence of an underlying cerebrovascular accident, a device with a ventilator and an indwelling urinary catheter, and hepatobiliary tract infection. Age greater than or equal to 70 years was an independent risk factor for XDRPA. Solid cancer was associated with a significantly lower risk of MDRPA and XDRPA.
Table 3

Risk factors associated with antimicrobial resistance in P. aeruginosa bacteremia

jkms-36-e273-i003
Risk factors Adjusted OR (95% CI) P
CRPA bacteremia
Underlying cerebrovascular accident 4.2 (1.8–9.6) 0.001
Previous exposure to
Carbapenems 4.7 (1.6–13.5) 0.005
Fluoroquinolones 2.7 (1.3–5.7) 0.009
Glycopeptides 3.1 (1.1–8.8) 0.034
Urinary tract infection 4.1 (1.9–8.8) < 0.001
MDRPA bacteremia
Underlying cerebrovascular accident 2.5 (1.1–5.5) 0.029
Underlying solid cancer 0.5 (0.3–0.9) 0.017
Device with ventilator 3.6 (1.2–10.7) 0.021
With indwelling urinary catheter 2.3 (1.1–4.6) 0.021
Previous exposure to
Fluoroquinolones 2.4 (1.2–4.8) 0.015
Glycopeptides 4.7 (1.9–11.2) 0.001
Urinary tract infection 4.8 (2.1–10.8) < 0.001
Hepatobiliary tract infection 13.2 (6.0–28.9) < 0.001
XDRPA bacteremia
Age ≥ 70 2.3 (1.1–4.8) 0.022
Underlying solid cancer 0.5 (0.2–1.0) 0.042
Previous exposure to
Fluoroquinolones 3.5 (1.7–7.5) 0.001
Glycopeptides 2.9 (1.2–7.1) 0.016
Urinary tract infection 3.6 (1.6–7.9) 0.001
OR = odds ratio, CI = confidence interval, CRPA = carbapenem-resistant P. aeruginosa, MDRPA = multidrug-resistant P. aeruginosa, XDRPA = extensively drug-resistant P. aeruginosa.

Clinical outcomes and risk factors for 30-day mortality

The 30-day mortality rate was 27.1% (80/295; Table 2). Of those, 56 (70.0%) had septic shock and 49 (61.3%) died within 5-day after bacteremia. Independent risk factors for 30-day mortality were the presence of underlying hematologic malignancy, ICU stay, polymicrobial bacteremia, septic shock, and respiratory tract as the primary site of infection (Table 4). The 30-day mortality did not differ between multidrug-resistant strains and non-multidrug-resistant strains (Table 2). In subgroup analysis for patients (169/295, 57.3%) without polymicrobial bacteremia, those receiving active antimicrobial therapy, and hospitalization for more than 5 days after the onset of bacteremia, antibiotic resistant strains and multidrug-resistant strains resulted in a higher 30-day mortality rate than strains susceptible to all antibiotics and non-multidrug-resistant strains, but this was not statistically significant (Fig. 4).
Table 4

Risk factors for 30-day mortality in patients with P. aeruginosa bacteremia

jkms-36-e273-i004
Characteristics Non-survivor (n = 80) Survivor (n = 215) Univariable HR (95% CI) P Multivariable HR (95% CI) P
Age ≥ 65 49 (61.3) 128 (59.5) 1.2 (0.8–1.9) 0.418
Male sex 59 (73.8) 138 (64.2) 0.7 (0.4–1.1) 0.146
Underlying conditions
Cardiovascular disease 9 (11.3) 21 (9.8) 1.0 (0.5–1.9) 0.892
Cerebrovascular accident 5 (6.3) 35 (16.3) 0.3 (0.1–0.9) 0.021
Chronic kidney disease 10 (12.5) 14 (6.5) 1.5 (0.8–3.0) 0.206
COPD or chronic lung disease 2 (2.5) 5 (2.3) 1.0 (0.3–4.2) 0.967
Dementia 1 (1.3) 13 (6.0) 0.3 (0.1–2.5) 0.294
Diabetes 26 (32.5) 58 (27.0) 1.2 (0.8–2.0) 0.384
Heart failure 5 (6.3) 9 (4.2) 1.9 (0.7–4.6) 0.183
Hypertension 27 (33.8) 85 (39.5) 0.8 (0.5–1.2) 0.270
Liver disease 3 (3.8) 9 (4.2) 0.9 (0.3–2.9) 0.882
Solid cancer 44 (55.0) 111 (51.6) 1.1 (0.7–1.8) 0.512
Hematologic malignancy 16 (20.0) 10 (4.7) 2.8 (1.6–4.9) < 0.001 2.3 (1.3–4.0) 0.005
Immunosuppressive therapy 35 (43.8) 67 (31.2) 1.5 (0.9–2.3) 0.071
Neutropenia 28 (35.0) 26 (12.1) 2.7 (1.7–4.3) < 0.001
Transplantation 2 (2.5) 5 (2.3) 1.1 (0.3–4.3) 0.931
CCI score ≥ 5 49 (61.3) 121 (56.3) 1.2 (0.8–1.9) 0.439
Healthcare-associated infection 65 (81.3) 180 (83.7) 0.8 (0.5–1.4) 0.400
Previous surgery within 90 days 20 (25.0) 46 (21.4) 1.1 (0.7–1.9) 0.587
Any antibiotic exposure within 90 days 65 (81.3) 156 (72.6) 1.3 (0.7–2.2) 0.397
Colonization with MDROs
CRE 1 (1.3) 3 (1.4) 1.6 (0.2–11.6) 0.638
ESBL 4 (5.0) 23 (10.7) 0.5 (0.2–1.2) 0.126
MRAB 9 (11.3) 7 (3.3) 2.0 (1.0–4.1) 0.049
MRSA 2 (2.5) 3 (1.4) 1.2 (0.3–5.2) 0.738
VRE 9 (11.3) 10 (4.7) 1.8 (0.9–3.6) 0.095
ICU stay 25 (31.3) 23 (10.7) 2.3 (1.5–3.8) < 0.001 1.7 (1.1–2.8) 0.025
Devices during time at risk
Central venous catheter 35 (43.8) 72 (33.5) 1.2 (0.8–1.9) 0.342
Ventilator 17 (21.3) 12 (5.6) 2.6 (1.5–4.5) < 0.001
Indwelling urinary catheter 34 (42.5) 66 (30.7) 1.4 (0.9–2.2) 0.132
Antimicrobial resistance
CRPA 22 (27.5) 51 (23.7) 1.1 (0.7–1.8) 0.774
MDRPA 28 (35.0) 78 (36.3) 0.9 (0.6–1.4) 0.669
XDRPA 14 (17.5) 33 (15.3) 1.0 (0.6–1.8) 0.904
Polymicrobial infection 21 (26.3) 29 (13.5) 1.8 (1.1–3.0) 0.017 1.8 (1.1–3.1) 0.032
Shock on the first day of bacteremia 56 (70.0) 28 (13.0) 8.5 (5.3–13.8) < 0.001 6.4 (3.8–10.7) < 0.001
Primary site of infection
Hepato-biliary tract 15 (18.8) 64 (29.8) 0.6 (0.4–1.1) 0.096
Gastrointestinal tract 7 (8.8) 12 (5.6) 1.2 (0.7–3.3) 0.283
Respiratory tract 35 (43.8) 34 (15.8) 3.4 (2.2–5.2) < 0.001 1.7 (1.0–2.8) 0.034
Urinary tract 7 (8.8) 43 (20.0) 0.4 (0.2–0.9) 0.023
Central venous catheter 7 (8.8) 32 (14.9) 0.5 (0.2–1.1) 0.088
Skin and soft tissue 2 (2.5) 10 (4.7) 0.5 (0.1–2.1) 0.372
Surgical site 1 (1.3) 1 (0.5) 3.3 (0.5–23.7) 0.239
Primary bloodstream 6 (7.5) 19 (8.8) 0.9 (0.4–2.1) 0.824
Invasive drainage procedures 9 (11.3) 53 (24.7) 0.5 (0.2–0.9) 0.024
Active antimicrobial therapy 67 (83.8) 177 (82.3) 0.7 (0.4–1.3) 0.292
Concordant empirical antimicrobial therapy 56 (70.0) 125 (58.1) 1.4 (0.9–2.3) 0.141
Single antibiotics 42 (52.5) 109 (50.7)
Aminoglycoside 0 (0) 6 (2.8)
Anti-pseudomonal cephalosporine 12 (15.0) 23 (10.7)
Anti-pseudomonal penicillin 5 (6.3) 39 (18.1)
Carbapenem 17 (21.3) 31 (14.4)
Fluoroquinolone 1 (1.3) 8 (3.7)
Colistin 7 (8.8) 2 (0.9)
Combination antibiotics 14 (17.5) 16 (7.4)
Anti-pseudomonal cephalosporine + aminoglycoside 1 (1.3) 2 (0.9)
Anti-pseudomonal cephalosporine + fluoroquinolone 1 (1.3) 1 (0.5)
Anti-pseudomonal penicillin + fluoroquinolone 9 (11.3) 12 (5.6)
Carbapenem + aminoglycoside 2 (2.5) 0 (0)
Carbapenem + fluoroquinolone 1 (1.3) 1 (0.5)
Values are presented as number (%).
HR = hazard ratio, CI = confidence interval, COPD = chronic obstructive pulmonary disease, CCI = Charlson comorbidity index, MDRO = multidrug-resistant organism, CRE = carbapenem-resistant Enterobacteriaceae, ESBL = extended-spectrum beta-lactamase-producing bacteria, MRAB = multi-drug resistant Acinetobacter baumannii, MRSA = methicillin-resistant Staphylococcus aureus, VRE = vancomycin-resistant Enterococcus, ICU = intensive care unit, CRPA = carbapenem-resistant P. aeruginosa, MDRPA = multidrug-resistant P. aeruginosa, XDRPA = extensively drug-resistant P. aeruginosa.
Fig. 4

Survival curve according to antibiotic resistance in patientsa with P. aeruginosa bacteremia.

CRPA = carbapenem-resistant P. aeruginosa, MDRPA = multidrug-resistant P. aeruginosa, XDRPA = extensively drug-resistant P. aeruginosa.
aPatients who did not show polymicrobial bacteremia, received active antimicrobial therapy during their hospital stay, and were hospitalized for more than 5 days after the onset of bacteremia.
jkms-36-e273-g004

DISCUSSION

Our study provides antimicrobial susceptibility trends in P. aeruginosa bacteremia and risk factors for their antimicrobial resistance in a tertiary hospital for 12 years. XDRPA showed an increasing trend, and CRPA, MDRPA, and XDRPA were also gradually increasing in the non-ICU setting. Previous exposure to fluoroquinolones and glycopeptides and urinary tract infection were independent risk factors associated with CRPA, MDRPA, and XDRPA. CRPA, MDRPA, and XDRPA were associated with discordant empirical antimicrobial therapy.
The report of Kor-GLASS, a surveillance system for antimicrobial resistance in general hospitals and nursing hospitals, on blood samples from general hospitals in 2017 showed that the rates of P. aeruginosa susceptibility to amikacin, meropenem, ceftazidime, piperacillin-tazobactam, and ciprofloxacin were 92.6%, 77.9%, 84.6%, 81.2%, and 83.9%.13 MDRPA and XDRPA increased slightly, from 15% and 11% in 2016 to 19.2% and 15.4%, respectively in 2019.29 However, in blood samples from nursing hospitals in 2019, the rates of CRPA, MDRPA, and XDRPA were 56.3%, 71.9%, and 62.5%, respectively, which were higher than those of general hospitals.29 The results of our study showed higher resistance to ceftazidime, piperacillin-tazobactam, and fluoroquinolones and higher rates of MDRPA and XDRPA than the results of general hospitals identified by Kor-GLASS. Korean National Healthcare-associated Infections Surveillance System (KONIS) reports revealed that the rate of imipenem-resistant P. aeruginosa increased since 2017 (45% from July 2013 to June 2017 vs. 51.1% from July 2017 to June 2020).30 Although comparisons with KONIS reports are limited because blood and other specimens are not separated, our study showed a slight decrease in CRPA and MDRPA during 2017–2020 compared with that during 2013–2016. In 2016, in the ICU of our hospital, the CRE increased sharply. Subsequently, to prevent the spread of multidrug-resistant bacteria, multifaceted strategies were applied, such as active surveillance culture of CRE; separating the ICU into a clean zone, waiting zone, and MDRO zone; donning protective equipment for all patients; access control; enhancing environmental cleaning; strengthening infection control monitoring; and antimicrobial stewardship. Our multifaceted efforts might have reduced the spread of resistant bacteria. However, further studies are needed to assess whether these strategies were effective in reducing antibiotic-resistant P. aeruginosa infection in the ICU. Additionally, it is necessary to provide antimicrobial susceptibility results classified by region and hospital for infection control and early active antimicrobial therapy.
Previous studies have reported prior exposure to fluoroquinolones and carbapenems and prior hospital stay as risk factors for MDRPA and XDRPA.1415 Moreover, MDRPA was significantly associated with a prior ICU stay, highly invasive device scores, bedridden state, and prior exposure to aminoglycosides and cephalosporins, and XDRPA was associated with receiving total parenteral nutrition and hematologic malignancy.1516 Risk factors for CRPA were prior exposure to fluoroquinolones, carbapenem, piperacillin-tazobactam, and vancomycin and an indwelling catheter.14181931 Similar to the results of previous studies, our study showed that prior exposure to fluoroquinolone was a risk factor for CRPA, MDRPA, and XDRPA and devices with ventilator and indwelling urinary catheters were risk factors for MDRPA. In addition, urinary tract infection and prior exposure to glycopeptides were independent risk factors of CRPA, MDRPA, and XDRPA. In our study, patients with solid cancers showed a significantly lower risk of MDRPA and XDRPA. Approximately half of these patients did not receive chemotherapy and 20% of these patients were identified with P. aeruginosa bacteremia during hospitalization for the evaluation of recently confirmed cancer. They had short hospital stays before bacteremia and infrequently required devices such as an indwelling urinary catheter and ventilators. Although these findings seemed reasonable based on our results, further follow-up studies are necessary.
Several studies have shown that infections due to antibiotic-resistant P. aeruginosa are associated with high mortality rates.1518193233 Bug-drug mismatches are common in multidrug-resistant strains, and delayed active antibiotics are associated with poor prognosis.6 Contrary to previous research results, in our study, we did not identify the adverse effects of discordant empirical antimicrobial therapy and antibiotic resistance on mortality in P. aeruginosa bacteremia. This could be due to the effect of disease severity, primary site of infection, and virulence of pathogens rather than antibiotic resistance or appropriate antimicrobial therapy.1734353637 In our study, the mortality rate was high in seriously ill patients with septic shock on the first day of bacteremia, and 61.3% of deaths occurred within 5 days. Urinary tract infection and hepatobiliary tract infection were more common than respiratory tract infection as the causative site of CRPA, MDRPA, and XDRPA. Moreover, most of the patients with hepatobiliary tract infection received appropriate drainage procedures. We did not conduct a case-control study that adjusted for disease severity and underlying disease. Further, it was difficult to determine the effects of concordant empirical antimicrobial therapy because the frequency of antibiotic use, such as that of colistin, was low in patients with XDRPA bacteremia. However, CRPA, MDRPA, and XDRPA were significantly associated with discordant empirical antimicrobial therapy. Although disease severity, bacterial virulence, and primary site of infection are uncontrollable risk factors for mortality, the adverse effects of delayed active antimicrobial therapy, which has been identified in several studies, might be improved by identifying risk factors.356 Most new antibiotics recommended in the treatment guidelines for multidrug-resistant gram negative pathogens, such as ceftazidime-avibactam and imipenem-relebactam, are not available, and ceftolozane/tazobactam is not covered by insurance in Korea.11 Thus, empirical antibiotic therapy including colistin might be considered for patients with urinary tract infection with a history of prior exposure to fluoroquinolones and glycopeptides, especially in elderly patients or patients with a history of cerebrovascular accident. In particular, XDR infection should be considered for patients who have been transferred from nursing hospitals, considering the antimicrobial susceptibility trends of the region and hospital.29 Further studies are needed on effects of colistin-containing empirical antibiotics based on risk factors for antibiotic-resistant P. aeruginosa on microbiological and clinical treatment failure and mortality.
Our study had several limitations. First, this study was a single-center study conducted at a tertiary university hospital in the southeastern region of Korea, and the number of P. aeruginosa samples was relatively small; therefore, our results might not be extrapolated to other hospitals and regions of the country. Second, because it was a retrospective study, we cannot rule out unmeasured uncertainty, such as hopeless discharge or hospice care in patients with cancer. Moreover, the severity score on the first day of bacteremia, excluding shock, could not be measured and the type of antibiotic used before transport to the hospital might not be accurate. Third, recent studies reported a problem with the antimicrobial susceptibility testing of colistin, and the European Committee on Antimicrobial Susceptibility Testing and CLSI both recommend broth microdilution for colistin susceptibility tests.38 In our study, this method was not applied in colistin sensitivity tests; therefore, the colistin sensitivity results might not be accurate.
In conclusion, P. aeruginosa infections with multidrug-resistance strains are gradually increasing in Korea. The identification of antimicrobial susceptibility trends and risk factors for antibiotic resistance could be important for providing concordant empirical antimicrobial therapy to patients with P. aeruginosa infection. Further studies on the outcomes of empirical antimicrobial treatment based on risk factors for antibiotic-resistant P. aeruginosa are warranted.

ACKNOWLEDGMENTS

The results of this study were presented at ID Week 2019 in Washington, D.C., USA on 2–6 October 2019 (Abstract No. 181).

Notes

Funding: This work was supported by the 2019 Inje University research grant.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

  • Conceptualization: Kang JS, Moon C.

  • Data curation: Kang JS, Mun SJ.

  • Formal analysis: Kang JS.

  • Funding acquisition: Kang JS.

  • Investigation: Kang JS.

  • Methodology: Kang JS.

  • Supervision: Moon C, Lee S, Lee SH, Lee JE, Lee SO.

  • Validation: Kang JS.

  • Writing - original draft: Kang JS.

  • Writing - review & editing: Kang JS, Moon C, Mun SJ, Lee S, Lee SH, Lee JE, Lee SO.

References

1. Parkins MD, Gregson DB, Pitout JD, Ross T, Laupland KB. Population-based study of the epidemiology and the risk factors for Pseudomonas aeruginosa bloodstream infection. Infection. 2010; 38(1):25–32. PMID: 20012908.
2. Callejas-Díaz A, Fernández-Pérez C, Ramos-Martínez A, Múñez-Rubio E, Sánchez-Romero I, Vargas Núñez JA. Impact of Pseudomonas aeruginosa bacteraemia in a tertiary hospital: mortality and prognostic factors. Med Clin (Barc). 2019; 152(3):83–89. PMID: 29885868.
3. Kang CI, Kim SH, Kim HB, Park SW, Choe YJ, Oh MD, et al. Pseudomonas aeruginosa bacteremia: risk factors for mortality and influence of delayed receipt of effective antimicrobial therapy on clinical outcome. Clin Infect Dis. 2003; 37(6):745–751. PMID: 12955633.
4. Suárez C, Peña C, Tubau F, Gavaldà L, Manzur A, Dominguez MA, et al. Clinical impact of imipenem-resistant Pseudomonas aeruginosa bloodstream infections. J Infect. 2009; 58(4):285–290. PMID: 19297028.
5. Buehrle DJ, Shields RK, Clarke LG, Potoski BA, Clancy CJ, Nguyen MH. Carbapenem-resistant Pseudomonas aeruginosa bacteremia: risk factors for mortality and microbiologic treatment failure. Antimicrob Agents Chemother. 2016; 61(1):e01243-16. PMID: 27821456.
crossref
6. Kadri SS, Adjemian J, Lai YL, Spaulding AB, Ricotta E, Prevots DR, et al. Difficult-to-treat resistance in gram-negative bacteremia at 173 US hospitals: retrospective cohort analysis of prevalence, predictors, and outcome of resistance to all first-line agents. Clin Infect Dis. 2018; 67(12):1803–1814. PMID: 30052813.
crossref
7. Merchant S, Proudfoot EM, Quadri HN, McElroy HJ, Wright WR, Gupta A, et al. Risk factors for Pseudomonas aeruginosa infections in Asia-Pacific and consequences of inappropriate initial antimicrobial therapy: a systematic literature review and meta-analysis. J Glob Antimicrob Resist. 2018; 14:33–44. PMID: 29454906.
8. Centers for Disease Control and Prevention (CDC). Antibiotic resistance threats in the United States, 2013. Updated 2013. Accessed July 15, 2021. https://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf.
9. Centers for Disease Control and Prevention (CDC). Antibiotic resistance threats in the United States, 2019. Updated 2019. Accessed July 15, 2021. https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf.
10. Horcajada JP, Montero M, Oliver A, Sorlí L, Luque S, Gómez-Zorrilla S, et al. Epidemiology and treatment of multidrug-resistant and extensively drug-resistant Pseudomonas aeruginosa Infections. Clin Microbiol Rev. 2019; 32(4):e00031-19. PMID: 31462403.
crossref
11. Tamma PD, Aitken SL, Bonomo RA, Mathers AJ, van Duin D, Clancy CJ. Infectious Diseases Society of America guidance on the treatment of extended-spectrum β-lactamase producing Enterobacterales (ESBL-E), carbapenem-resistant Enterobacterales (CRE), and Pseudomonas aeruginosa with difficult-to-treat resistance (DTR-P. aeruginosa). Clin Infect Dis. 2021; 72(7):e169–e183. PMID: 33106864.
12. Korea Centers for Disease Control and Prevention (KCDC). Korean Antimicrobial Resistance Monitoring System: KARMS 2016 Annual Report. Cheongju, Korea: KCDC;2018.
13. Liu C, Yoon EJ, Kim D, Shin JH, Shin JH, Shin KS, et al. Antimicrobial resistance in South Korea: a report from the Korean Global Antimicrobial Resistance Surveillance System (Kor-GLASS) for 2017. J Infect Chemother. 2019; 25(11):845–859. PMID: 31311694.
crossref
14. Raman G, Avendano EE, Chan J, Merchant S, Puzniak L. Risk factors for hospitalized patients with resistant or multidrug-resistant Pseudomonas aeruginosa infections: a systematic review and meta-analysis. Antimicrob Resist Infect Control. 2018; 7:79. PMID: 29997889.
crossref
15. Aloush V, Navon-Venezia S, Seigman-Igra Y, Cabili S, Carmeli Y. Multidrug-resistant Pseudomonas aeruginosa: risk factors and clinical impact. Antimicrob Agents Chemother. 2006; 50(1):43–48. PMID: 16377665.
16. Palavutitotai N, Jitmuang A, Tongsai S, Kiratisin P, Angkasekwinai N. Epidemiology and risk factors of extensively drug-resistant Pseudomonas aeruginosa infections. PLoS One. 2018; 13(2):e0193431. PMID: 29470531.
17. Samonis G, Vardakas KZ, Kofteridis DP, Dimopoulou D, Andrianaki AM, Chatzinikolaou I, et al. Characteristics, risk factors and outcomes of adult cancer patients with extensively drug-resistant Pseudomonas aeruginosa infections. Infection. 2014; 42(4):721–728. PMID: 24912861.
18. Lee CH, Su TY, Ye JJ, Hsu PC, Kuo AJ, Chia JH, et al. Risk factors and clinical significance of bacteremia caused by Pseudomonas aeruginosa resistant only to carbapenems. J Microbiol Immunol Infect. 2017; 50(5):677–683. PMID: 26188977.
19. Joo EJ, Kang CI, Ha YE, Kang SJ, Park SY, Chung DR, et al. Risk factors for mortality in patients with Pseudomonas aeruginosa bacteremia: clinical impact of antimicrobial resistance on outcome. Microb Drug Resist. 2011; 17(2):305–312. PMID: 21381966.
20. Clinical and Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing. 29th ed. Wayne, PA, USA: CLSI;2019.
21. Yo CH, Hsein YC, Wu YL, Hsu WT, Ma MH, Tsai CH, et al. Clinical predictors and outcome impact of community-onset polymicrobial bloodstream infection. Int J Antimicrob Agents. 2019; 54(6):716–722. PMID: 31560960.
crossref
22. Haque M, Sartelli M, McKimm J, Abu Bakar M. Health care-associated infections - an overview. Infect Drug Resist. 2018; 11:2321–2333. PMID: 30532565.
23. Revelas A. Healthcare - associated infections: a public health problem. Niger Med J. 2012; 53(2):59–64. PMID: 23271847.
crossref
24. Centers for Disease Control and Prevention (CDC). CDC/NHSN surveillance definitions for specific types of infections. Updated 2021. Accessed July 15, 2021. https://www.cdc.gov/nhsn/pdfs/pscmanual/17pscnosinfdef_current.pdf.
25. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016; 315(8):801–810. PMID: 26903338.
crossref
26. Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012; 18(3):268–281. PMID: 21793988.
crossref
27. Aryee A, Rockenschaub P, Gill MJ, Hayward A, Shallcross L. The relationship between clinical outcomes and empirical antibiotic therapy in patients with community-onset gram-negative bloodstream infections: a cohort study from a large teaching hospital. Epidemiol Infect. 2020; 148:e225. PMID: 32912362.
crossref
28. Kadri SS, Lai YL, Warner S, Strich JR, Babiker A, Ricotta EE, et al. Inappropriate empirical antibiotic therapy for bloodstream infections based on discordant in-vitro susceptibilities: a retrospective cohort analysis of prevalence, predictors, and mortality risk in US hospitals. Lancet Infect Dis. 2021; 21(2):241–251. PMID: 32916100.
crossref
29. Korean Disease Control and Prevention Agency (KDCA). National Antimicrobial Resistance Surveillance in Korea 2019 Annual Report. Cheongju, Korea: KDCA;2020.
30. Korean National Healthcare-associated Infections Surveillance System (KONIS). ICU reports. Updated 2021. Accessed May 1, 2021. http://konis.cafe24.com/xe/reports_icu_y.
31. Tuon FF, Gortz LW, Rocha JL. Risk factors for pan-resistant Pseudomonas aeruginosa bacteremia and the adequacy of antibiotic therapy. Braz J Infect Dis. 2012; 16(4):351–356. PMID: 22846123.
32. Nathwani D, Raman G, Sulham K, Gavaghan M, Menon V. Clinical and economic consequences of hospital-acquired resistant and multidrug-resistant Pseudomonas aeruginosa infections: a systematic review and meta-analysis. Antimicrob Resist Infect Control. 2014; 3(1):32. PMID: 25371812.
crossref
33. Bassetti M, Vena A, Croxatto A, Righi E, Guery B. How to manage Pseudomonas aeruginosa infections. Drugs Context. 2018; 7:212527. PMID: 29872449.
34. Montero MM, López Montesinos I, Knobel H, Molas E, Sorlí L, Siverio-Parés A, et al. Risk factors for mortality among patients with Pseudomonas aeruginosa bloodstream infections: What is the influence of XDR phenotype on outcomes? J Clin Med. 2020; 9(2):514.
35. Kim YJ, Jun YH, Kim YR, Park KG, Park YJ, Kang JY, et al. Risk factors for mortality in patients with Pseudomonas aeruginosa bacteremia; retrospective study of impact of combination antimicrobial therapy. BMC Infect Dis. 2014; 14:161. PMID: 24655422.
crossref
36. Peña C, Cabot G, Gómez-Zorrilla S, Zamorano L, Ocampo-Sosa A, Murillas J, et al. Influence of virulence genotype and resistance profile in the mortality of Pseudomonas aeruginosa bloodstream infections. Clin Infect Dis. 2015; 60(4):539–548. PMID: 25378459.
37. Jeong SJ, Yoon SS, Bae IK, Jeong SH, Kim JM, Lee K. Risk factors for mortality in patients with bloodstream infections caused by carbapenem-resistant Pseudomonas aeruginosa: clinical impact of bacterial virulence and strains on outcome. Diagn Microbiol Infect Dis. 2014; 80(2):130–135. PMID: 25112900.
38. Matuschek E, Åhman J, Webster C, Kahlmeter G. Antimicrobial susceptibility testing of colistin - evaluation of seven commercial MIC products against standard broth microdilution for Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter spp. Clin Microbiol Infect. 2018; 24(8):865–870. PMID: 29221995.
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