Abstract
Background
Drug-resistant Pseudomonas aeruginosa infections rapidly increased and contributed to life-threatening nosocomial infections; however, the distribution, species, drug susceptibility and dynamic trends of P. aeruginosa infection in China remained unclear. This study was conducted to better understand the epidemiological data of increased P. aeruginosa infections from 2016 to 2022 in a hospital in China.
Methods
This study involved 3301 patients infected with P. aeruginosa, diagnosed using a nosocomial infection surveillance system in a tertiary hospital between 2016 and 2022. The P. aeruginosa infections from 2016 to 2022 were assessed according to the hospital department and species, and the drug susceptibility was evaluated using 16 antimicrobial agents.
Results
The P. aeruginosa infection prevalence in the hospital department was: Neurosurgery (14.30%), Emergency (13.30%), and Critical Care Medicine (11.69%). Samples for P. aeruginosa infection identification were from sputum (72.52%) and other secreta (9.91%). The P. aeruginosa infections demonstrated a greater sensitivity to amikacin (AMK, 91.82%), tobramycin (TOB, 82.79%), and gentamycin (GEN, 82.01%); however, P. aeruginosa infection demonstrated greater resistance to ticarcillin (22.57%), levofloxacin (21.63%), and ciprofloxacin (18.00%).
Conclusion
The P. aeruginosa infections were commonly observed in the Neurosurgery, Emergency, and Critical Care Medicine departments and demonstrated greater sensitivity to AMK, TOB, and GEN than the other drugs.
Keywords: Pseudomonas aeruginosa, distribution, drug susceptibility, dynamic trend
Introduction
Health care-associated infections caused by nosocomial pathogens have become an important cause of morbidity and mortality.1 Pseudomonas aeruginosa is considered as a major pathogen for community-acquired and nosocomial infections worldwide and is capable of causing acute life-threatening infections in elderly, critically ill, and immunocompromised patients.2 Studies have reported that P. aeruginosa accounts for 7.1% of healthcare-associated infections in the United States, and P. aeruginosa was considered the most common pathogen (17%) for healthcare-associated pneumonia in the European Union.3,4 Moreover, P. aeruginosa consistently induced chronic pulmonary infection in patients with chronic wounds or cystic fibrosis.5,6
The organism’s limited susceptibility to antimicrobial agents may result in P. aeruginosa-induced life-threatening infections that are difficult to treat.7 Thus, P. aeruginosa has been classified as “critical” on the global priority pathogens list according to World Health Organization.8 Potential reasons for the increased antibiotic resistance of P. aeruginosa may include frequent use of antibiotics that could cause bacterial strains to acquire the ability to overcome drug inhibition and lethality. Moreover, various inherited mechanisms may contribute to the reduced efficacy of antibiotics; these include mutations of target structures, inactivation of antibiotic enzymes, and reduced intracellular concentrations.9 Therefore several factors contributed to the development of multi-drug resistant P. aeruginosa, and the rate of reported multi-drug resistant P. aeruginosa has increased from 4% to 14% from 1993 to 2002 in the United States, and this was regarded as a serious concern for hospitalized patients.10 Patients with multi-drug resistant P. aeruginosa infection and restricted drug options were associated with an increased risk of mortality.11,12
In China, as with other countries, P. aeruginosa was the most common gram-negative strain.13 However, prior studies focused on the incidence of P. aeruginosa infection, while the species, drug susceptibility and dynamic trends of P. aeruginosa infection in China were not addressed.14 In this study, we assessed 3301 patients infected with P. aeruginosa and determined their susceptibility profiles against 16 commonly used antimicrobial agents. The prevalence of P. aeruginosa infection by hospital department or species, and the drug susceptibility of 16 antimicrobial agents were evaluated between 2016 and 2022.
Subjects and Methods
Hospital Setting and Reagents
A total of 3301 patients infected with P. aeruginosa in a tertiary hospital between 2016 and 2022 were retrospectively enrolled in this study. This study was approved by the Medical Ethics Committee of the Handan Central Hospital (no: 202247). The quality control strain, P. aeruginosa, was purchased from the National Institute for Identification of Pharmaceutical and Biological Products (NICPBP; No: ATCC27853) of China. Strains isolated multiple times from the same patient were only counted once. Following routine isolation and culture, VITEC2 and GN identification plates provided by Meriai were used for strain identification. The blood plate and Chinese blue plate were provided by Tianjin Jinzhang Company (Tianjin, China), and the drug-sensitivity disks were purchased from Oxoid (Basingstoke, UK). The drug-sensitivity disks included the following drugs: amikacin (AMK), aztreonam, tobramycin (TOB), sulfamethoxazole tablets, ciprofloxacin (CIP), meropenem (MEM), piperacillin, tazobactam (TZP), gentamycin (GEN), ceftazidime (CAZ), cefotaxime, ceftriaxone, levofloxacin (LVX), imipenem (IPM), ticarcillin (TCC), cefepime (FEP), cefoperazone, colistin (COL), and polyoxin (POL).
Strain Isolation and Identification
The samples investigated were obtained from ascites, bile, bronchial alveolar fluid, blood, catheters, pleuritic, purulent material, secreta, spinal fluid, sputum, stool, other fluids drained, throat swabs, tissues, and urine. Hospital departments that samples were sourced from included the Pediatric, Otolaryngology, Orthopedics, Respiratory Medicine, Emergency, Rehabilitation, Geriatrics, Urinary surgery, Endocrinology, General surgery, Burn and plastic surgery, Neurology, Neurosurgery, Nephrology, Cardiovascular, Thoracic surgery, Oncology, Critical Care Medicine, and Other departments. Specimens were cultured and isolated in accordance with the third edition of National Clinical Laboratory Procedures. Following incubation at 35 °C for 18‒24 h, mucinous P. aeruginosa grows on the blood plate as colorless, small dew drops, with irregular edges, non-hemolytic small colonies; following 48 h of growth and fusion, viscous, jelly-like colonies, with hemolytic atypical, dispensable appearance were observed. The colonies without metallic luster and special odor were detected by inoculum ring, separated and identified as P. aeruginosa using VITEK2Compact (Meriai, France), which was further defined as mucinous P. aeruginosa. Following incubation at 35 °C for 18‒24 h, mucous patina grew into large, flat, moist, metallic luster, blue-green, transparent hemolytic ring colonies on the blood plate, with a ginger taste. Following purification, mucinous P. aeruginosa previously identified using VITEK2Compact, was then defined as non-mucinous P. aeruginosa.
Drug Susceptibility Test and Result Interpretation
The standard Kirby–Bauer concentration disk method was used to test the susceptibility to 16 common antibiotics. P. aeruginosa ATCC27853 was used for quality control. Non-mucinous P. aeruginosa was interpreted from the 24 h results. Due to the slow growth of mucinous P. aeruginosa the results of conventional 24 h interpretation will result in false negatives. Therefore, the results of the 48 h culture or the 24 h blood agar culture (MH plate) were used, as mucinous P. aeruginosa grows well on blood MH plates. All interpretations were performed in accordance with the 2011 standards of the American Clinical Laboratory Standards Committee.
Statistical Analysis
The distribution of P. aeruginosa infection by hospital department and samples are reported as events and proportions. Similarly, the drug susceptibility for 16 antimicrobial agents for each year is presented as events and proportions. The results of the P. aeruginosa infection drug susceptibility analysis were divided into resistance, intermediate sensitivity, and sensitive. The distribution, species, and drug susceptibility for each year were compared using the Chi-square test, and the dynamic trends of P. aeruginosa infection from 2016 to 2022 were assessed using the Spearman correlation coefficient. All of the reported p-values are two-sided, and the significance level was set at p = 0.05. All statistical analyses were conducted by using SPSS for Windows 24.0 (SPSS for Windows 24.0, SPSS, Chicago, IL, USA).
Results
P. aeruginosa Infection According to the Hospital Department
The distribution of P. aeruginosa infection from each hospital department is expressed in Table 1. Overall, P. aeruginosa infection was more frequently observed in the Neurosurgery (14.30%), Emergency (13.30%), and Critical Care Medicine (11.69%) departments. When stratified by year, P. aeruginosa infection was more frequently detected in the Neurosurgery (15.05%), Emergency (13.07%), and Critical Care Medicine (11.88%) departments in 2016; the Neurosurgery (16.91%), Critical Care Medicine (11.27%), and Emergency (11.06%) departments in 2017; the Emergency (14.71%), Neurosurgery (12.82%), and Critical Care Medicine (11.13%) departments in 2018; the Neurosurgery (15.03%), Emergency (12.06%), and Respiratory Medicine (11.69%) departments in 2019; the Critical Care Medicine (13.69%), Neurosurgery (13.49%), and Emergency (13.29%) departments in 2020; the Emergency (13.68%), Neurosurgery (12.87%), and Critical Care Medicine (12.21%) in 2021; and the Emergency (18.48%), Neurosurgery (14.13%), and Critical Care Medicine (10.87%) departments in 2022. There were no significant association for the distribution of P. aeruginosa infection according to department with trends over time (p > 0.05).
Table 1.
Distribution of Pseudomonas aeruginosa in Each Department
| Department | 2016 (n=505) | 2017 (n=479) | 2018 (n=476) | 2019 (n=539) | 2020 (n=504) | 2021 (n=614) | 2022 (n=184) | Total (n=3301) |
|---|---|---|---|---|---|---|---|---|
| Pediatric | 25 (4.95%) | 25 (5.22%) | 27 (5.67%) | 21 (3.90%) | 18 (3.57%) | 33 (5.37%) | 5 (2.72%) | 154 (4.67%) |
| Otolaryngology | 11 (2.18%) | 8 (1.67%) | 7 (1.47%) | 13 (2.41%) | 11 (2.18%) | 6 (0.98%) | 1 (0.54%) | 57 (1.73%) |
| Orthopedics | 20 (3.96%) | 22 (4.59%) | 24 (5.04%) | 16 (2.97%) | 13 (2.58%) | 14 (2.28%) | 1 (0.54%) | 110 (3.33%) |
| Respiratory Medicine | 58 (11.49%) | 47 (9.81%) | 39 (8.19%) | 63 (11.69%) | 41 (8.13%) | 62 (10.10%) | 18 (9.78%) | 328 (9.94%) |
| Emergency | 66 (13.07%) | 53 (11.06%) | 70 (14.71%) | 65 (12.06%) | 67 (13.29%) | 84 (13.68%) | 34 (18.48%) | 439 (13.30%) |
| Rehabilitation | 0 (0.00%) | 3 (0.63%) | 4 (0.84%) | 6 (1.11%) | 10 (1.98%) | 16 (2.61%) | 4 (2.17%) | 43 (1.30%) |
| Geriatrics | 22 (4.36%) | 25 (5.22%) | 20 (4.20%) | 32 (5.94%) | 30 (5.95%) | 36 (5.86%) | 10 (5.43%) | 175 (5.30%) |
| Urinary surgery | 6 (1.19%) | 21 (4.38%) | 23 (4.83%) | 11 (2.04%) | 7 (1.39%) | 4 (0.65%) | 1 (0.54%) | 73 (2.21%) |
| Endocrinology | 6 (1.19%) | 12 (2.51%) | 9 (1.89%) | 6 (1.11%) | 3 (0.60%) | 8 (1.30%) | 3 (1.63%) | 47 (1.42%) |
| General surgery | 17 (3.37%) | 17 (3.55%) | 16 (3.36%) | 31 (5.75%) | 41 (8.13%) | 34 (5.54%) | 11 (5.98%) | 167 (5.06%) |
| Burn and plastic surgery | 4 (0.79%) | 10 (2.09%) | 19 (3.99%) | 15 (2.78%) | 21 (4.17%) | 24 (3.91%) | 7 (3.80%) | 100 (3.03%) |
| Neurology | 24 (4.75%) | 18 (3.76%) | 16 (3.36%) | 19 (3.53%) | 12 (2.38%) | 14 (2.28%) | 10 (5.43%) | 113 (3.42%) |
| Neurosurgery | 76 (15.05%) | 81 (16.91%) | 61 (12.82%) | 81 (15.03%) | 68 (13.49%) | 79 (12.87%) | 26 (14.13%) | 472 (14.30%) |
| Nephrology | 13 (2.57%) | 7 (1.46%) | 9 (1.89%) | 8 (1.48%) | 7 (1.39%) | 15 (2.44%) | 8 (4.35%) | 67 (2.03%) |
| Cardiovascular | 24 (4.75%) | 14 (2.92%) | 11 (2.31%) | 22 (4.08%) | 12 (2.38%) | 16 (2.61%) | 1 (0.54%) | 100 (3.03%) |
| Thoracic surgery | 12 (2.38%) | 13 (2.71%) | 15 (3.15%) | 20 (3.71%) | 18 (3.57%) | 24 (3.91%) | 4 (2.17%) | 106 (3.21%) |
| Oncology | 33 (6.53%) | 21 (4.38%) | 22 (4.62%) | 33 (6.12%) | 26 (5.16%) | 30 (4.89%) | 5 (2.72%) | 170 (5.15%) |
| Critical Care Medicine | 60 (11.88%) | 54 (11.27%) | 53 (11.13%) | 49 (9.09%) | 69 (13.69%) | 75 (12.21%) | 20 (10.87%) | 386 (11.69%) |
| Other | 28 (5.54%) | 28 (5.85%) | 25 (5.25%) | 28 (5.19%) | 30 (5.95%) | 40 (6.51%) | 15 (8.15%) | 194 (5.88%) |
P. aeruginosa Infection According to the Sample Type
The distribution of P. aeruginosa infection according to sample type is presented in Table 2. The majority of confirmed P. aeruginosa infection samples were from sputum (72.52%), and secreta (9.91%). When stratified by years, P. aeruginosa infection was most commonly identified from sputum (77.62%) and secreta (8.12%) in 2016; from sputum (71.82%), secreta (11.48%), and urine (5.22%) in 2017; from sputum (68.07%), secreta (12.39%), and urine (6.30%) in 2018; from sputum (74.77%) and secreta (10.39%) in 2019; from sputum (69.64%), secreta (9.33%), and blood (6.35%) in 2020; from sputum (73.45%) and secreta (8.47%) in 2021; and from sputum (70.11%), secreta (9.24%), and bile (6.52%) in 2022. The distribution of P. aeruginosa infection according to type from 2016 to 2022 was associated with statistically significant (p < 0.05).
Table 2.
Distribution of Pseudomonas aeruginosa According to Specimen Type
| Specimen | 2016 (n=505) | 2017 (n=479) | 2018 (n=476) | 2019 (n=539) | 2020 (n=504) | 2021 (n=614) | 2022 (n=184) | Total (n=3301) |
|---|---|---|---|---|---|---|---|---|
| Ascites | 2 (0.40%) | 2 (0.42%) | 5 (1.05%) | 2 (0.37%) | 3 (0.60%) | 9 (1.47%) | 5 (2.72%) | 28 (0.85%) |
| Bile | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 10 (1.98%) | 13 (2.12%) | 12 (6.52%) | 35 (1.06%) |
| Bronchial alveolar | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1 (0.19%) | 0 (0.00%) | 4 (0.65%) | 0 (0.00%) | 5 (0.15%) |
| Blood | 12 (2.38%) | 15 (3.13%) | 13 (2.73%) | 10 (1.86%) | 32 (6.35%) | 15 (2.44%) | 3 (1.63%) | 100 (3.03%) |
| Catheter | 1 (0.20%) | 2 (0.42%) | 0 (0.00%) | 1 (0.19%) | 0 (0.00%) | 2 (0.33%) | 2 (1.09%) | 8 (0.24%) |
| Pleuritic | 3 (0.59%) | 4 (0.84%) | 4 (0.84%) | 1 (0.19%) | 3 (0.60%) | 3 (0.49%) | 0 (0.00%) | 18 (0.55%) |
| Purulent | 15 (2.97%) | 10 (2.09%) | 14 (2.94%) | 19 (3.53%) | 13 (2.58%) | 22 (3.58%) | 8 (4.35%) | 101 (3.06%) |
| Secreta | 41 (8.12%) | 55 (11.48%) | 59 (12.39%) | 56 (10.39%) | 47 (9.33%) | 52 (8.47%) | 17 (9.24%) | 327 (9.91%) |
| Spinal fluid | 3 (0.59%) | 2 (0.42%) | 3 (0.63%) | 1 (0.19%) | 0 (0.00%) | 0 (0.00%) | 2 (1.09%) | 11 (0.33%) |
| Sputum | 392 (77.62%) | 344 (71.82%) | 324 (68.07%) | 403 (74.77%) | 351 (69.64%) | 451 (73.45%) | 129 (70.11%) | 2394 (72.52%) |
| Stool | 1 (0.20%) | 2 (0.42%) | 1 (0.21%) | 3 (0.56%) | 1 (0.20%) | 3 (0.49%) | 0 (0.00%) | 11 (0.33%) |
| Drain | 8 (1.58%) | 10 (2.09%) | 16 (3.36%) | 9 (1.67%) | 8 (1.59%) | 4 (0.65%) | 0 (0.00%) | 55 (1.67%) |
| Throat swab | 8 (1.58%) | 6 (1.25%) | 6 (1.26%) | 7 (1.30%) | 10 (1.98%) | 4 (0.65%) | 0 (0.00%) | 41 (1.24%) |
| Tissue | 0 (0.00%) | 1 (0.21%) | 0 (0.00%) | 2 (0.37%) | 3 (0.60%) | 3 (0.49%) | 0 (0.00%) | 9 (0.27%) |
| Urine | 19 (3.76%) | 25 (5.22%) | 30 (6.30%) | 24 (4.45%) | 23 (4.56%) | 29 (4.72%) | 6 (3.26%) | 156 (4.73%) |
Antimicrobial Susceptibility Testing
The distribution of P. aeruginosa infection drug susceptibility is outlined in Table 3. The drug sensitivity to P. aeruginosa infection was greater for AMK (91.82%), TOB (82.79%), and GEN (82.01%), while the drug resistance to P. aeruginosa infection was greater for TCC (22.57%), LVX (21.63%), and CIP (18.00%). When stratified by year, the drug sensitivities to P. aeruginosa infection were greater for AMK (87.33%), COL (84.36%), and POL (82.38%) in 2016; for AMK (93.95%), COL (93.11%), and POL (91.02%) in 2017; for AMK (90.76%), MEM (84.66%), and TOB (82.98%) in 2018; for AMK (93.32%), TOB (87.38%), and GEN (86.46%) in 2019; for AMK (93.45%), TOB (84.72%), and FEP (83.93%) in 2020; for AMK (91.37%), POL (85.99%), and TOB (84.20%) in 2021; for AMK (94.02%), CAZ (86.96%), and MEM (86.41%) in 2022. Finally, we noted significant association of the drug susceptibility of P. aeruginosa infection according to the type of antibacterial with trends over time (p < 0.05).
Table 3.
Distribution of Drug Susceptibility for Pseudomonas aeruginosa
| Antibacterial | Category | 2016 (n=505) | 2017 (n=479) | 2018 (n=476) | 2019 (n=539) | 2020 (n=504) | 2021 (n=614) | 2022 (n=184) | Total (n=3301) |
|---|---|---|---|---|---|---|---|---|---|
| AMK | Resistance | 0 (0.00%) | 1 (0.21%) | 3 (0.63%) | 20 (3.71%) | 15 (2.98%) | 35 (5.70%) | 5 (2.72%) | 79 (2.39%) |
| Intermediate | 26 (5.15%) | 28 (5.85%) | 38 (7.98%) | 14 (2.60%) | 14 (2.78%) | 15 (2.44%) | 4 (2.17%) | 139 (4.21%) | |
| Sensitivity | 441 (87.33%) | 450 (93.95%) | 432 (90.76%) | 503 (93.32%) | 471 (93.45%) | 561 (91.37%) | 173 (94.02%) | 3031 (91.82%) | |
| ATM | Resistance | 0 (0.00%) | 3 (0.63%) | 17 (3.57%) | 42 (7.79%) | 41 (8.13%) | 98 (15.96%) | 26 (14.13%) | 227 (6.88%) |
| Intermediate | 191 (38.42%) | 180 (37.58%) | 181 (38.03%) | 34 (6.31%) | 18 (3.57%) | 54 (8.79%) | 14 (7.61%) | 672 (20.36%) | |
| Sensitivity | 270 (53.47%) | 285 (59.50%) | 258 (54.20%) | 250 (46.38%) | 215 (42.66%) | 383 (62.38%) | 137 (74.46%) | 1798 (54.47%) | |
| POL | Resistance | 1 (0.20%) | 0 (0.00%) | 1 (0.21%) | 1 (0.19%) | 0 (0.00%) | 0 (0.00%) | 3 (1.63%) | 6 (0.18%) |
| Intermediate | 31 (6.14%) | 34 (7.10%) | 95 (19.96%) | 319 (59.18%) | 261 (51.79%) | 528 (85.99%) | 171 (92.93%) | 1439 (43.59%) | |
| Sensitivity | 416 (82.38%) | 436 (91.02%) | 366 (76.89%) | 5 (0.93%) | 1 (0.20%) | 5 (0.81%) | 3 (1.63%) | 1232 (37.32%) | |
| CIP | Resistance | 93 (18.42%) | 96 (20.04%) | 90 (18.91%) | 86 (15.96%) | 96 (19.05%) | 117 (19.06%) | 36 (19.57%) | 614 (18.60%) |
| Intermediate | 371 (73.47%) | 379 (79.12%) | 378 (79.41%) | 285 (52.88%) | 229 (45.44%) | 434 (70.68%) | 142 (77.17%) | 2218 (67.19%) | |
| Sensitivity | 0 (0.00%) | 1 (0.21%) | 3 (0.63%) | 164 (30.43%) | 175 (34.72%) | 59 (9.61%) | 3 (1.63%) | 405 (12.27%) | |
| MEM | Resistance | 67 (13.27%) | 59 (12.32%) | 62 (13.03%) | 22 (4.08%) | 14 (2.78%) | 28 (4.56%) | 7 (3.80%) | 259 (7.85%) |
| Intermediate | 21 (4.16%) | 18 (3.76%) | 10 (2.10%) | 11 (2.04%) | 6 (1.19%) | 18 (2.93%) | 11 (5.98%) | 95 (2.88%) | |
| Sensitivity | 369 (73.07%) | 398 (81.21%) | 403 (84.66%) | 291 (53.99%) | 252 (50.00%) | 489 (79.64%) | 159 (86.41%) | 2361 (71.52%) | |
| PIP | Resistance | 62 (12.28%) | 98 (20.46%) | 100 (21.01%) | 55 (10.20%) | 39 (7.74%) | 96 (15.64%) | 24 (13.04%) | 474 (14.36%) |
| Intermediate | 38 (7.52%) | 44 (9.19%) | 44 (9.24%) | 25 (4.64%) | 25 (4.96%) | 61 (9.93%) | 12 (6.52%) | 249 (7.54%) | |
| Sensitivity | 360 (71.29%) | 324 (67.64%) | 325 (68.28%) | 241 (44.71%) | 208 (41.27%) | 371 (60.42%) | 137 (74.46%) | 1966 (59.56%) | |
| TZP | Resistance | 50 (9.90%) | 64 (13.36%) | 53 (11.13%) | 52 (9.65%) | 37 (7.34%) | 59 (9.61%) | 17 (9.24%) | 332 (10.06%) |
| Intermediate | 34 (6.73%) | 42 (8.77%) | 56 (11.76%) | 73 (13.54%) | 65 (12.90%) | 77 (12.54%) | 14 (7.61%) | 361 (10.94%) | |
| Sensitivity | 380 (75.25%) | 366 (76.41%) | 350 (73.53%) | 411 (76.25%) | 397 (78.77%) | 475 (77.36%) | 151 (82.07%) | 2530 (76.64%) | |
| GEN | Resistance | 0 (0.00%) | 1 (0.21%) | 9 (1.89%) | 43 (7.98%) | 42 (8.33%) | 74 (12.05%) | 24 (13.04%) | 193 (5.85%) |
| Intermediate | 95 (18.81%) | 82 (17.12%) | 72 (15.13%) | 26 (4.82%) | 32 (6.35%) | 20 (3.26%) | 6 (3.26%) | 333 (10.09%) | |
| Sensitivity | 370 (73.27%) | 394 (82.25%) | 393 (82.56%) | 466 (86.46%) | 420 (83.33%) | 513 (83.55%) | 151 (82.07%) | 2707 (82.01%) | |
| TCC | Resistance | 173 (34.26%) | 128 (26.72%) | 161 (33.82%) | 74 (13.73%) | 62 (12.30%) | 118 (19.22%) | 29 (15.76%) | 745 (22.57%) |
| Intermediate | 234 (46.34%) | 179 (37.37%) | 178 (37.39%) | 121 (22.45%) | 106 (21.03%) | 188 (30.62%) | 64 (34.78%) | 1070 (32.41%) | |
| Sensitivity | 49 (9.70%) | 33 (6.89%) | 47 (9.87%) | 130 (24.12%) | 103 (20.44%) | 226 (36.81%) | 83 (45.11%) | 671 (20.33%) | |
| FEP | Resistance | 0 (0.00%) | 2 (0.42%) | 8 (1.68%) | 47 (8.72%) | 45 (8.93%) | 63 (10.26%) | 13 (7.07%) | 178 (5.39%) |
| Intermediate | 85 (16.83%) | 109 (22.76%) | 110 (23.11%) | 45 (8.35%) | 30 (5.95%) | 48 (7.82%) | 15 (8.15%) | 442 (13.39%) | |
| Sensitivity | 379 (75.05%) | 365 (76.20%) | 353 (74.16%) | 445 (82.56%) | 423 (83.93%) | 500 (81.43%) | 154 (83.70%) | 2619 (79.34%) | |
| CSL | Resistance | 8 (1.58%) | 6 (1.25%) | 10 (2.10%) | 0 (0.00%) | 18 (3.57%) | 87 (14.17%) | 24 (13.04%) | 153 (4.63%) |
| Intermediate | 12 (2.38%) | 1 (0.21%) | 1 (0.21%) | 0 (0.00%) | 19 (3.77%) | 78 (12.70%) | 30 (16.30%) | 141 (4.27%) | |
| Sensitivity | 31 (6.14%) | 23 (4.80%) | 28 (5.88%) | 0 (0.00%) | 121 (24.01%) | 317 (51.63%) | 123 (66.85%) | 643 (19.48%) | |
| CAZ | Resistance | 0 (0.00%) | 1 (0.21%) | 11 (2.31%) | 72 (13.36%) | 62 (12.30%) | 93 (15.15%) | 18 (9.78%) | 257 (7.79%) |
| Intermediate | 91 (18.02%) | 102 (21.29%) | 101 (21.22%) | 33 (6.12%) | 36 (7.14%) | 37 (6.03%) | 4 (2.17%) | 404 (12.24%) | |
| Sensitivity | 376 (74.46%) | 372 (77.66%) | 362 (76.05%) | 432 (80.15%) | 401 (79.56%) | 480 (78.18%) | 160 (86.96%) | 2583 (78.25%) | |
| TOB | Resistance | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) |
| Intermediate | 94 (18.61%) | 79 (16.79%) | 81 (17.02%) | 65 (12.06%) | 73 (14.48%) | 93 (15.15%) | 32 (17.39%) | 517 (15.66%) | |
| Sensitivity | 373 (73.86%) | 400 (83.51%) | 395 (82.98%) | 471 (87.38%) | 427 (84.72%) | 517 (84.20%) | 150 (81.52%) | 2733 (82.79%) | |
| IPM | Resistance | 38 (7.52%) | 43 (8.98%) | 55 (11.55%) | 88 (16.33%) | 107 (21.23%) | 136 (22.15%) | 28 (15.22%) | 495 (15.00%) |
| Intermediate | 51 (10.10%) | 36 (7.52%) | 26 (5.46%) | 29 (5.38%) | 13 (2.58%) | 14 (2.28%) | 8 (4.35%) | 177 (5.36%) | |
| Sensitivity | 367 (72.67%) | 392 (81.84%) | 389 (81.72%) | 416 (77.18%) | 378 (75.00%) | 456 (74.27%) | 146 (79.35%) | 2544 (77.07%) | |
| COL | Resistance | 1 (0.20%) | 0 (0.00%) | 2 (0.42%) | 1 (0.19%) | 0 (0.00%) | 0 (0.00%) | 2 (1.09%) | 6 (0.18%) |
| Intermediate | 11 (2.18%) | 19 (3.97%) | 95 (19.96%) | 317 (58.81%) | 260 (51.59%) | 523 (85.18%) | 171 (92.93%) | 1396 (42.29%) | |
| Sensitivity | 426 (84.36%) | 446 (93.11%) | 362 (76.05%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1234 (37.38%) | |
| LVX | Resistance | 124 (24.55%) | 117 (24.43%) | 129 (27.10%) | 96 (17.81%) | 97 (19.25%) | 116 (18.89%) | 35 (19.02%) | 714 (21.63%) |
| Intermediate | 72 (14.26%) | 50 (10.44%) | 101 (21.22%) | 271 (50.28%) | 231 (45.83%) | 434 (70.68%) | 142 (77.17%) | 1301 (39.41%) | |
| Sensitivity | 267 (52.87%) | 306 (63.88%) | 243 (51.05%) | 169 (31.35%) | 172 (34.13%) | 61 (9.93%) | 4 (2.17%) | 1222 (37.02%) |
Discussion
This retrospective study involved 3301 patients infected with P. aeruginosa and aimed to assess the distribution, species, drug susceptibility and dynamic trends of P. aeruginosa infection in a tertiary hospital based on the results of a nosocomial infection surveillance system. The characteristics of the patients included varied, and the study covered a broad range of patient diseases. The P. aeruginosa infections were more frequently identified in the departments of Neurosurgery (14.30%), Emergency (13.30%), and Critical Care Medicine (11.69%). Moreover, the sample from which P. aeruginosa infection was most commonly identified included sputum (72.52%) and secreta (9.91%). Finally, P. aeruginosa infection drug sensitivity was greater for AMK (91.82%), TOB (82.79%), and GEN (82.01%) than for the other drugs tested.
Several studies have investigated the distribution, species, and drug susceptibility of P. aeruginosa infections. Cui et al identified 9381 episodes of bacteremia during 2010‒2019 and determined that P. aeruginosa infection decreased from 4.0‒2.4%, which was consistent with the China Antimicrobial Surveillance Network report in 2018.14 Lila et al identified 553 P. aeruginosa isolates from the University Clinical Centre of Kosovo and reported that P. aeruginosa was the second most frequently isolated hospital pathogen, and samples were primarily isolated from the Intensive Care Unit (68.7%). Moreover, the most frequent body system from which P. aeruginosa was isolated was the respiratory tract (58.4%). Furthermore, antimicrobials evaluated demonstrated increased resistance, particularly the carbapenems, IPM and MEM.15 Wan et al identified 4306 types of pathogens from the hematology results of 26 tertiary hospitals and reported that P. aeruginosa accounted for 8.50% of the infections.16 However, studies have not focused on the distribution, species, drug susceptibility and dynamic trends of P. aeruginosa infections in China. Therefore, the current study intended to systematically describe the status of P. aeruginosa infection and drug susceptibility in China.
The P. aeruginosa infections were most frequently identified in the departments of Neurosurgery, Emergency, and Critical Care Medicine. A greater prevalence of patients were from the departments of Neurosurgery, Emergency, and Critical Care Medicine, and they were admitted to the Intensive Care Unit; intensive care patients were an important risk factor for infection likely to the debilitating effects of prolonged hospitalization and the regular administration of medicines and use of medical equipment.17–19 Additionally, the most common sample from which P. aeruginosa infections were isolated were sputum and secreta. This may have been due to the frequency of P. aeruginosa colonization that occurred in bronchiectasis, which was able to induce airway inflammation and tissue destruction.20,21 The P. aeruginosa was the dominant microorganism isolated from sputum samples in patients with bronchiectasis.22–24 These results suggesting patients from the departments of Neurosurgery, Emergency, and Critical Care Medicine should received more frequent surveillance to identify P. aeruginosa infections, and these departments needed strengthen to further reduce P. aeruginosa infections. Moreover, the sputum and secreta samples should be applied to identify P. aeruginosa infections in clinical practice, especially in the departments at high risk for P. aeruginosa infections.
Drug sensitivity for P. aeruginosa infection was greater for AMK, TOB, and GEN, while the drug-resistance in P. aeruginosa infections was greater for TCC, LVX, and CIP. Studies have demonstrated the prevalence of antimicrobial resistance in different geographical settings, a greater prevalence of resistance in P. aeruginosa in the eastern and south-eastern parts of Europe in particular. Moreover, national prevalence of resistant isolates ranged from 4.4% to 58.5%, specifically in Netherlands to Romania, and the prevalence of resistance trends in Germany, Hungary, and Slovakia were significantly increased.25 Antibiotics are increasingly becoming resistant to P. aeruginosa isolates, and the beta-lactam resistance was the highest in the United States, Europe, and South America.26 Finally, the drug susceptibility trends of COL and POL was significantly reduced, and the drug sensitivities of AMK was persisted from 2016 to 2022, which could explained by the prevalence of intermediate were significantly increased for COL and POL.
Several studies have outlined risk factors for drug-resistant P. aeruginosa infection in China. Rao et al reported that IPM treatment within two weeks of age was a significant independent risk factor for IPM-resistant P. aeruginosa in neonatal intensive care units.27 Gao et al recruited 747 patients with bronchiectasis and determined that the risk factors for P. aeruginosa resistance included prior exposure to antibiotics, three or more exacerbations in the previous year, higher modified Medical Research Council dyspnea scores and greater radiologic severity.28 Hu et al determined that the risk factors carbapenem-resistant P. aeruginosa included patients aged over 60 years of age, particularly those in intensive care units.29 The high resistance to TCC, LVX, and CIP of P. aeruginosa infection could be explained by the carbapenem resistant isolates of P. aeruginosa that were associated with cross-resistance to TCC, LVX, and CIP.30 Moreover, the use of TCC, LVX, and CIP were most common in the study hospital, these were associated with high drug-resistance in P. aeruginosa infections.
Several limitations of this study are acknowledged. Firstly, individual patient characteristics varies, which could affect the use of antimicrobial agents. Secondly, the severity of disease differs among patients, which may affect the drug susceptibility in P. aeruginosa infections. Thirdly, the analyses were based on a retrospective design, and the results could be affected by selection and recall biases. Fourthly, all of patients from the single-hospital, and the generalizing of results should be cautious. Finally, the dynamic trends of P. aeruginosa infection were not modified by the potential confounders, and further investigations may consider focusing on specific patients.
Conclusion
Infections with P. aeruginosa frequently originated from the departments of Neurosurgery, Emergency, or Critical Care Medicine. Additionally, sputum and secreta samples had greater infection prevalence than the other samples. Drug resistance in P. aeruginosa infection was greater for TCC, LVX, and CIP, while the sensitivity was greater for AMK, TOB, and GEN. These findings may guide clinicians in the management of P. aeruginosa infections. Further large-scale study based on multi-hospital should be performed to verify the results of this study and analyzing the susceptibility trends over time.
Abbreviations
AMK, Amikacin; TOB, tobramycin; CIP, ciprofloxacin; MEM, meropenem; TZP, tazobactam, GEN, gentamycin; CAZ, ceftazidime; LVX, levofloxacin; IPM, imipenem; TCC, ticarcillinticarcillin; FEP, cefepime; COL, colistin; POL, polyoxin.
Data Sharing Statement
The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author at a reasonable request.
Ethics Approval and Consent to Participate
Our study complies with the Declaration of Helsinki. This study was approved by the Medical Ethics Committee of the Handan Central Hospital (no: 202247). Informed consent was obtained from all individual participants included in the study.
Disclosure
The authors declare that they have no competing interests.
References
- 1.Ong DS, Jongerden IP, Buiting AG, et al. Antibiotic exposure and resistance development in Pseudomonas aeruginosa and Enterobacter species in intensive care units. Crit Care Med. 2011;11:2458–2463. doi: 10.1097/CCM.0b013e318225756d [DOI] [PubMed] [Google Scholar]
- 2.Sadikot RT, Blackwell TS, Christman JW, et al. Pathogen-host interactions in Pseudomonas aeruginosa pneumonia. Am J Respir Crit Care Med. 2005;171(11):1209–1223. doi: 10.1164/rccm.200408-1044SO [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Magill SS, Edwards JR, Bamberg W, et al. Multistate point-prevalence survey of health care-associated infections. N Engl J Med. 2014;370:1198–1208. doi: 10.1056/NEJMoa1306801 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Walter J, Haller S, Quinten C, et al. Healthcare-associated pneumonia in acute care hospitals in European Union/European Economic Area countries: an analysis of data from a point prevalence survey, 2011 to 2012. Euro Surveill. 2018;23:1700843. doi: 10.2807/1560-7917.ES.2018.23.32.1700843 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Waters CM, Goldberg JB. Pseudomonas aeruginosa in cystic fibrosis: a chronic cheater. Proc Natl Acad Sci U S A. 2019;116:6525–6527. doi: 10.1073/pnas.1902734116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Malhotra S, Hayes D, Wozniak DJ. Cystic fibrosis and Pseudomonas aeruginosa: the host-microbe interface. Clin Microbiol Rev. 2019;32:e00138–e00118. doi: 10.1128/CMR.00138-18 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Carmeli Y, Troillet N, Eliopoulos GM, et al. Emergence of antibiotic-resistant Pseudomonas aeruginosa: comparison of risks associated with different antipseudomonal agents. Antimicrob Agents Chemother. 1999;43:1379–1382. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Tamma PD, Aitken SL, Bonomo RA, et al. 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:e169–e183. doi: 10.1093/cid/ciaa1478 [DOI] [PubMed] [Google Scholar]
- 9.Thöming JG, Häussler S. Pseudomonas aeruginosa is more tolerant under biofilm than under planktonic growth conditions: a multi-isolate survey. Front Cell Infect Microbiol. 2022;12:851784. doi: 10.3389/fcimb.2022.851784 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Obritsch MD, Fish DN, MacLaren R, et al. National surveillance of antimicrobial resistance in Pseudomonas aeruginosa isolates obtained from intensive care unit patients from 1993 to 2002. Antimicrob Agents Chemother. 2004;12:4606–4610. doi: 10.1128/AAC.48.12.4606-4610.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Obritsch MD, Fish DN, MacLaren R, et al. Nosocomial infections due to multidrug-resistant Pseudomonas aeruginosa: epidemiology and treatment options. Pharmacotherapy. 2005;10(10):1353–1364. doi: 10.1592/phco.2005.25.10.1353 [DOI] [PubMed] [Google Scholar]
- 12.Giamarellos-Bourboulis EJ, Papadimitriou E, Galanakis N, et al. Multidrug resistance to antimicrobials as a predominant factor influencing patient survival. Int J Antimicrob Agents. 2006;6:476–481. doi: 10.1016/j.ijantimicag.2005.12.013 [DOI] [PubMed] [Google Scholar]
- 13.Li Y, Lv Y, Zheng B. Ministry of Health National Antimicrobial Resistance Investigation Net annual report of 2011: surveillance of antimicrobial resistance in nonfermenting gram-negative bacteria in China. Chin J Clin Pharmacol. 2012;12:883–887. [Google Scholar]
- 14.Cui J, Li M, Cui J, et al. The proportion, species distribution and dynamic trends of bloodstream infection cases in a tertiary hospital in China, 2010–2019. Infection. 2022;50(1):121–130. doi: 10.1007/s15010-021-01649-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lila G, Mulliqi-Osmani G, Bajrami R, et al. The prevalence and resistance patterns of Pseudomonas aeruginosa in a tertiary care hospital in Kosovo. Infez Med. 2017;25(1):21–26. [PubMed] [Google Scholar]
- 16.Wan YK, Sang W, Chen B, et al. Distribution and drug resistance of pathogens at hematology department of Jiangsu Province from 2014 to 2015: results from a multicenter, retrospective study. Zhonghua Xue Ye Xue Za Zhi. 2017;38:602–606. doi: 10.3760/cma.j.issn.0253-2727.2017.07.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Gales AC, Torres PL, Vilarinho DS, et al. Carbapenem-resistant Pseudomonas aeruginosa outbreak in an intensive care unit of a teaching hospital. Braz J Infect Dis. 2004;8(4):267–271. doi: 10.1590/S1413-86702004000400001 [DOI] [PubMed] [Google Scholar]
- 18.Savas L, Duran N, Savas N, et al. The prevalence and resistance patterns of pseudomonas aeruginosa in intensive care units in a University Hospital. Turk J Med Sci. 2005;35:317–322. [Google Scholar]
- 19.Wunderink R, Mendoza D. Epidemiology of Pseudomonas aeruginosa in the intensive care unit. In: Rello J, Kollef M, Diaz E, Rodriguez A, editors. Infectious Diseases in Critical Care. Berlin Heidelberg: Springer; 2007:218–225. [Google Scholar]
- 20.Chalmers JD, Smith MP, McHugh BJ, et al. Short- and long-term antibiotic treatment reduces airway and systemic inflammation in non-cystic fibrosis bronchiectasis. Am J Respir Crit Care Med. 2012;186(7):657–665. doi: 10.1164/rccm.201203-0487OC [DOI] [PubMed] [Google Scholar]
- 21.Guan WJ, Gao YH, Xu G, et al. Sputum matrix metalloproteinase-8 and −9 and tissue inhibitor of metalloproteinase-1 in bronchiectasis: clinical correlates and prognostic implications. Respirology. 2015;20:1073–1081. doi: 10.1111/resp.12582 [DOI] [PubMed] [Google Scholar]
- 22.Angrill J, Agusti C, de Celis R, et al. Bacterial colonisation in patients with bronchiectasis: microbiological pattern and risk factors. Thorax. 2002;57:15–19. doi: 10.1136/thorax.57.1.15 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Guan WJ, Gao YH, Xu G, et al. Sputum bacteriology in steady-state bronchiectasis in Guangzhou, China. Int J Tubercul Lung Dis. 2015;19(5):610–619. doi: 10.5588/ijtld.14.0613 [DOI] [PubMed] [Google Scholar]
- 24.Guan WJ, Gao YH, Xu G, et al. Effect of airway Pseudomonas aeruginosa isolation and infection on steady-state bronchiectasis in Guangzhou, China. J Thorac Dis. 2015;7:625–636. doi: 10.3978/j.issn.2072-1439.2015.04.04 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.European Centre for Disease Prevention and Control. Annual epidemiological reports; 2014. Available from: http://ecdc.europa.eu/en/publications/Publications/antimicrobial-resistance-annual-epidemiological-report.pdf. Accessed August 20, 2022.
- 26.Kos VN, Déraspe M, McLaughlin RE, et al. The resistome of Pseudomonas aeruginosa in relationship to phenotypic susceptibility. Antimicrob Agents Chemother. 2015;59:427–436. doi: 10.1128/AAC.03954-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Rao YB, Ren ZX, Zhong JJ, et al. Risk factors for imipenem-resistant Pseudomonas aeruginosa in neonatal intensive care units in south China. J Hosp Infect. 2018;98:305–308. doi: 10.1016/j.jhin.2017.12.016 [DOI] [PubMed] [Google Scholar]
- 28.Gao YH, Guan WJ, Zhu YN, et al. Antibiotic-resistant Pseudomonas aeruginosa infection in patients with bronchiectasis: prevalence, risk factors and prognostic implications</em>. Int J Chron Obstruct Pulmon Dis. 2018;13:237–246. doi: 10.2147/COPD.S150250 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hu YY, Cao JM, Yang Q, et al. Risk factors for carbapenem-resistant Pseudomonas aeruginosa, Zhejiang Province, China. Emerg Infect Dis. 2019;25:1861–1867. doi: 10.3201/eid2510.181699 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Odoi H, Boamah VE, Duah Boakye Y, et al. Sensitivity patterns, plasmid profiles and clonal relatedness of multi-drug resistant Pseudomonas aeruginosa isolated from the Ashanti Region, Ghana. Environ Health Insights. 2022;16:11786302221078117. doi: 10.1177/11786302221078117 [DOI] [PMC free article] [PubMed] [Google Scholar]