Hostname: page-component-76d6cb85b7-dqfph Total loading time: 0 Render date: 2026-07-16T23:08:39.285Z Has data issue: false hasContentIssue false
  • English
  • Français

Worldwide threatening prevalence of carbapenem-resistant Pseudomonas aeruginosa

Published online by Cambridge University Press:  19 September 2025

Mohammad Hossein Ahmadi Open the ORCID record for Mohammad Hossein Ahmadi [Opens in a new window] ,
Zeinab Fagheei Aghmiyuni  and
Shahriar Bakhti
Mohammad Hossein Ahmadi*
Affiliation:
Department of Microbiology, Faculty of Medicine, Shahed University , Tehran, Iran
Zeinab Fagheei Aghmiyuni
Affiliation:
Department of Microbiology, Faculty of Medicine, Shahed University , Tehran, Iran
Shahriar Bakhti
Affiliation:
Department of Microbiology, Faculty of Medicine, Shahed University , Tehran, Iran
*
Corresponding author: Mohammad Hossein Ahmadi; Email: mhahmadi@shahed.ac.ir
Rights & Permissions [Opens in a new window]

Abstract

β-Lactam/β-lactamase inhibitor combinations and carbapenems are the first-line treatments for multidrug-resistant Pseudomonas aeruginosa (P. aeruginosa) infections. However, carbapenem resistance is increasing globally at an alarming rate, which is especially concerning given the pivotal role of these agents. This study comprehensively evaluated the global distribution of carbapenem resistance in clinical P. aeruginosa isolates. The keywords including ‘Pseudomonas’, P. aeruginosa’, ‘P. aeruginosa’, ‘resistance’, ‘susceptibility’, ‘carbapenem antibiotics’, ‘carbapenems’, ‘imipenem’, ‘meropenem’, ‘ertapenem’, ‘doripenem’, as well as ‘prevalence’ and ‘incidence’ were searched in electronic databases as the appropriate keywords. After screening, 160 studies were excluded, with 87 eligible studies from diverse geographic regions retained for final analysis. A comprehensive meta-analysis was then conducted on the data collected. The mean resistance rates (95% CI) were 33.3% (imipenem), 23.3% (meropenem), 60.9% (ertapenem), and 36.7% (doripenem). The time trend analysis showed that the resistance to meropenem has increased from the year 1997 to 2023. Meta-analysis showed substantial heterogeneity (I2 = 92%, p < 0.05) but no significant publication bias by Egger’s or Begg’s test. Global carbapenem resistance is alarmingly high in clinical P. aeruginosa isolates. The increasing prevalence of carbapenem-resistant P. aeruginosa is a major global health threat requiring urgent action through new antimicrobials and improved antibiotic stewardship to protect these last-line drugs.

Keywords

carbapenem resistancedoripenemertapenemimipenemmeropenemP. aeruginosa

Information

Type
Original Paper
Information
Epidemiology & Infection , Volume 153 , 2025 , e114
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Introduction

Pseudomonas aeruginosa (P. aeruginosa) is a Gram-negative, aerobic, non-fermentative bacillus and opportunistic pathogen [Reference Terahara and Nishiura1–Reference Yin3]. This ubiquitous bacterium thrives in moist environments and poses a significant threat in healthcare settings [Reference Terahara and Nishiura1, Reference Abdeta2]. It commonly causes urinary tract infections, surgical site infections, healthcare-associated pneumonia, and bloodstream infections [Reference Terahara and Nishiura1, Reference Abdeta2, Reference Kaluba4]. P. aeruginosa causes high mortality, especially in cystic fibrosis patients with chronic infections, burn victims, and the immunocompromised individuals [Reference Yin3–Reference Reynolds and Kollef6] P. aeruginosa infections are managed through prevention, bacterial culture, and early antimicrobial therapy [Reference Reynolds and Kollef6].

Antimicrobial therapy is the primary method for controlling bacterial infections [Reference Terahara and Nishiura1, Reference Yin3, Reference Reynolds and Kollef6]. Antipseudomonal antibiotics, including carbapenems, fluoroquinolones, and aminoglycosides, treat severe infections. Their misuse can increase antimicrobial resistance [Reference Terahara and Nishiura1]. P. aeruginosa develops antibiotic resistance through drug inactivation, efflux pumps, and modifications to target sites or outer membranes [Reference Chatterjee7].

P. aeruginosa strains with extensive drug resistance (XDR), defined as susceptibility to only one or two antibiotic classes, and multidrug resistance (MDR), resistance across ≥3 antimicrobial categories, pose an increasing global health threat [Reference Horcajada8]. The 2018 difficult-to-treat resistance classification for P. aeruginosa identifies isolates non-susceptible to all first-line antibiotics, including carbapenems (imipenem-cilastatin, meropenem), antipseudomonal β-lactams (ceftazidime, cefepime, and piperacillin-tazobactam), fluoroquinolones (ciprofloxacin, levofloxacin), and aztreonam [Reference Kadri9]. A 2020 ECDC (European Center for Disease Prevention and Control) report showed concerning antibiotic resistance in P. aeruginosa, with 30.1% of isolates resistant to at least one agent in five core antimicrobial classes and 17.3% exhibiting cross-resistance to at least two [Reference Karruli10].

Carbapenems are last-line antibiotics for severe multidrug-resistant infections [11]. Carbapenems are broad-spectrum ß-lactam antibiotics that include doripenem, ertapenem, imipenem, and meropenem [11]. P. aeruginosa’s carbapenemase genes confer carbapenem resistance, significantly reducing the effectiveness of common antipseudomonal agents [Reference Tenover, Nicolau and Gill12]. Carbapenemases in P. aeruginosa, including Class A (KPC, GES), metallo-β-lactamases (IMP, NDM, SPM, VIM), and Class D (OXA-48) enzymes, exhibit regional variation [Reference Tenover, Nicolau and Gill12]. Carbapenem-resistant P. aeruginosa (CRPA) infections exhibit resistance to multiple antibiotics [Reference Wei13]. In healthcare settings, CRPA spreads easily between patients via contaminated hands, surfaces, or equipment [Reference Reynolds and Kollef6].

The World Health Organization (WHO) has nominated CRPA as a life-threatening-priority pathogen, highlighting the urgent need for new antimicrobial drugs [Reference Shrivastava, Shrivastava and Ramasamy14]. Prevalence studies to prevent and monitor CRPA distribution exist, but published reports often focus on single healthcare facilities or geographical locations within countries [Reference Terahara and Nishiura1]. Therefore, globally assessing the prevalence of CRPA strains in clinical isolates is crucial.

This study used a PRISMA-guided meta-analysis to estimate carbapenem resistance prevalence in clinical P. aeruginosa isolates worldwide [Reference Moher15].

Methods

Search strategy

This meta-analysis searched Web of Science, Scopus, PubMed, and Google Scholar from January 1997 to May 2023 for original English articles reporting the prevalence or incidence of carbapenem-resistant P. aeruginosa. Keywords included ‘Pseudomonas’, ‘P. aeruginosa’, ‘P. aeruginosa’, ‘resistance’, ‘susceptibility’, ‘carbapenem antibiotics’, ‘carbapenems’, ‘imipenem’, ‘meropenem’, ‘ertapenem’, ‘doripenem’, ‘prevalence’, and ‘incidence’, combined with Boolean operators (AND, OR).

Study selection framework

This study included original research articles reporting P. aeruginosa resistance rates to carbapenems (imipenem, meropenem, ertapenem, and doripenem). Excluded were meta-analyses, reviews, theses, studies using non-human samples, articles lacking sufficient data for analysis, reports of genus-level (not species-level) antibiotic resistance, irrelevant titles, congress abstracts, and duplicate publications.

Data extraction

The extracted dataset included publication year, study design, first author name, geographic region, participant demographics, specimen category, total isolates, resistance detection method, and resistant isolate counts per antibiotic. Two independent reviewers extracted data, resolving discrepancies through discussion.

Statistical analysis

Carbapenem resistance prevalence was analysed using Comprehensive Meta-Analysis software (v3.7z, Bio stat) and expressed as pooled estimates with 95% confidence intervals. Heterogeneity was assessed using Cochran’s Q statistic and I2 statistic. If substantial heterogeneity was present (I2 > 50%), a random-effects model was used. Publication bias was evaluated via funnel plot symmetry, Begg’s test, and Egger’s test (p < 0.05). Each study was assigned a relative weight, and a meta-regression using the random-effect model (method of moments) has been performed [Reference Kelley and Kelley16, Reference Thompson and Higgins17].

Bivariate linear regression using GraphPad Prism (v9.5) assessed temporal trends in P. aeruginosa carbapenem resistance (1997–2023), with average resistance rates and 95% confidence intervals reported. For antibiotics with sufficient data, the linear regression equation and R2 value were also calculated.

Results

Database searches (Web of Science, Scopus, Google Scholar, and PubMed) yielded 247 articles. After title screening, 119 articles were excluded (61 duplicates and 58 irrelevant titles). During subordinate screening, 21 publications were omitted due to either non-human specimen or article type (reviews). Full-text evaluation led to the exclusion of 20 more studies due to genus-level reporting (no species-level antibiotic resistance data) and/or deficient data. Ultimately, 87 articles published from January 1997 to May 2023 were included in the final analysis (Figure 1 and Table 1).

Figure 1.

Flow chart of the literature search, systematic review and study selection.

The ultimate analysis merged data from 41 countries worldwide, including 49 Asian studies, 13 European investigations, 14 African reports, and 15 North/South American datasets (Table 1). The study population comprised diverse male and female patients from multiple care settings, outpatient clinics, general hospital wards, and intensive care units, all with confirmed P. aeruginosa infections. The included patients with hospital-acquired and healthcare-associated P. aeruginosa infections, across multiple clinical presentations: intra-abdominal, pulmonary, cutaneous/soft tissue, bloodstream, surgical site, urinary tract, as well as burn-related, cystic fibrosis-associated, COVID-19, and osteomyelitis cases (Table 1). Samples in the included studies consisted of blood, sputum, endotracheal secretions, bronchoalveolar lavage fluid, catheter tips, urine (mid-stream and catheterized), nasopharyngeal specimens, throat, ear, and eye swabs, abscess/wound samples, tissue, body fluid, and cerebrospinal fluid (CSF) (Table 1). Antimicrobial susceptibility testing in these studies focused on imipenem, meropenem, ertapenem, and doripenem. Mean resistance rates for P. aeruginosa isolates were 33.3% (imipenem), 23.3% (meropenem), 60.9% (ertapenem), and 36.7% (doripenem) (95% CI). Figures 2–4 show the forest plots of the meta-analysis for resistance rate of P. aeruginosa to the antibiotics. Table 2 shows the range and pooled resistance rates of P. aeruginosa isolates to antibiotics.

Table 1.

Studies included in meta-analysis after final evaluation

Abbreviations: NM: not measured; NS: not specified; ICU: Intensive care unit; CSF: cerebrospinal fluid; ET: Endo-tracheal tract secretions, BAL: bronchoalveolar lavage; CT: Catheter tip; UTI: urinary tract infection.

Figure 2.

Forest plot of the meta-analysis of resistance rate of P. aeruginosa to imipenem.

Figure 3.

Forest plot of the meta-analysis of resistance rate of P. aeruginosa to meropenem.

Figure 4.

Forest plots of the meta-analysis of resistance rate to ertapenem (a) and doripenem (b) for P. aeruginosa.

Table 2.

Meta-analysis results for resistance rate of Pseudomonas aeruginosa in included studies

Note: a: Kendall’s tau without continuity correction; b: Kendall’s tau with continuity correction.

a Pooled resistance rate (based on random effects).

b Begg and Mazumdar rank correlation.

c Egger’s regression intercept.

Time trend analysis (Pearson correlation) revealed a significant increase in P. aeruginosa resistance to meropenem from 1997 to 2023 (r = 0.593, p = 0.002, 95% CI: 0.2491–0.8040), indicating a strong correlation with year of publication. Figure 5 illustrates the resistance rates of imipenem and meropenem between 1997 and 2023.

Figure 5.

The resistance rates of P. aeruginosa to meropenem (a) and imipenem (b) during the years 1997 to 2023. Dashed lines represent 95% confidence intervals (CI: 0.2491–0.8040).

Table 2 shows mid-range resistance rates in P. aeruginosa isolates: imipenem (50%), meropenem (50%), ertapenem (73.3%), and doripenem (46.3%). Significant heterogeneity was present across all studies (I2 > 50%, P < 0.05), but no publication bias was detected (Figure 6, Table 2, Egger’s and Begg’s tests). Fewer than 10 articles investigated ertapenem and doripenem resistance, precluding further analysis.

Figure 6.

Funnel plots of the meta-analysis for the resistance rate of P. aeruginosa isolates to carbapenems: imipenem (a), meropenem (b), ertapenem (c), and doripenem (d).

Discussion

Our analysis found substantial geographic difference in P. aeruginosa resistance to clinically important carbapenems (imipenem, meropenem, ertapenem, and doripenem), with reliably elevated resistance rates observed globally. Carbapenem-resistant P. aeruginosa (CRPA) has been downgraded in WHO Bacterial Priority Pathogens List (BPPL) from ‘critical’ to ‘high priority’ in the 2024 update, reflecting a potential global decrease in resistance observed in at least one WHO region and its relatively lower transmission compared to other carbapenem-resistant strains. These results are consistent with resistance patterns reported in the Global Burden of Disease Antimicrobial Resistance study [Reference Murray105].

Ertapenem was investigated due to its potential to reduce the use of imipenem, meropenem, and doripenem in antimicrobial stewardship programs without increasing resistance in Enterobacteriaceae and P. aeruginosa. However, despite ertapenem’s potential benefit in mitigating carbapenem resistance in P. aeruginosa, our study revealed a scarcity of research evaluating its antimicrobial activity. This may be due to some authors supporting ertapenem as an approach to decrease resistance to meropenem and imipenem [Reference Zequinão106].

Based on our evaluations, contributor characteristics, situations, sample types, antimicrobial susceptibility procedures, sample sizes, and study preference were highly heterogeneous. Geographical differences in P. aeruginosa antibiotic resistance frequencies likely come from differing antibiotic prescribing and consumption patterns across populations. Therefore, the important CRPA burden in high-income areas, particularly Central/Eastern Europe and Central Asia, demands urgent continued investment in targeted antimicrobial development [Reference Murray105, Reference Aguilar107, Reference Burke108]. Furthermore, its high mortality rate among immunocompromised individuals and in healthcare settings necessitates ongoing innovative research and development to mitigate its impact. The WHO BPPL guides global surveillance by prioritizing critical threats [109, 110].

ß-Lactams are broad-spectrum antibiotics often used empirically for sepsis, with carbapenems possessing a particularly broad antimicrobial spectrum [Reference Umemura111]. Carbapenem use in patients with extended-spectrum ß-lactamase-producing pathogen infections is linked to lower mortality, according to studies. Carbapenems are the preferred first-line antibiotics for sepsis, but their increasing global use poses a serious public health threat [Reference Umemura111]. This threat requires that the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) should continue monitoring CRPA for emerging resistance and intervention effectiveness [109].

The substantial heterogeneity and varying sample sizes across included studies may have influenced our analysis. The relative weight was calculated for each study and reported in the present study to overcome this problem. This study was limited by its restriction to English-language sources, potentially excluding relevant research. Furthermore, the meta-analysis, constrained by fewer than ten articles on P. aeruginosa antimicrobial resistance to ertapenem and doripenem, lacked statistical power and precluded definitive conclusions.

This meta-analysis was limited by the variation in breakpoints and methods used to define antimicrobial susceptibility and resistance across different studies. CLSI recommended standard-specific zone diameter and MIC breakpoints for imipenem and meropenem in P. aeruginosa antibiotic susceptibility testing in 2011 (M100-S21) and 2022 (M100-Ed32) [112, 113]. The meta-analysis included studies from 1997 to 2023. Studies prior to 2011 and 2022 used different antimicrobial susceptibility testing guidelines (non-M100-S21, M100-Ed32), resulting in variations in breakpoints and thresholds for susceptible and resistant strain determination.

The studies in our analysis exclusively used phenotypic methods for antibiotic susceptibility evaluation. These methods encompass classical culture-dependent techniques (disk diffusion assay, gradient diffusion method) yielding results in 18–48 h and automated microdilution systems providing faster results (6–24 h post-isolation). Fast bacterial identification and antibiotic susceptibility testing are crucial to combatting drug resistance, but traditional culture-based methods are time-consuming. Necessity often compels physicians to prescribe antibiotics empirically, contributing to inappropriate use, increased mortality and costs, and the spread of antibiotic resistance [Reference Li, Yang and Zhao114]. To expedite patient treatment and outcomes, continuous development of rapid antibiotic susceptibility and microorganism identification methods is crucial. Traditional methods like broth microdilution and disk diffusion remain essential in clinical practice to ensure accurate results that align with EUCAST and CLSI standards or to validate new methods [Reference Benkova, Soukup and Marek115].

Combating antibiotic resistance in P. aeruginosa and other pathogens necessitates coordinated global efforts, continuous monitoring, and responsive control programs. Developing new anti-Pseudomonas therapeutics is a key challenge in pharmaceutical science, alongside the pursuit of rapid diagnostic methods. Intensively studied methods for supporting the human immune system include preventative vaccines, therapeutic antibodies, and the development of antimicrobial peptides [Reference Dorotkiewicz-Jach116]. Antimicrobial agents encompass novel antibiotics, ß-lactamase and efflux pump inhibitors, quorum-dissolving molecules, and antibacterial nanoparticles. Developing alternative therapies like phage and photodynamic therapies, which employ mechanisms distinct from traditional antibiotics, is crucial.

In conclusion, the findings of the present study indicate that the overall resistance to carbapenems in clinical isolates of P. aeruginosa is relatively high and prevalent throughout the world. Moreover, time trend analysis showed that the resistance to meropenem has increased from the year 1997 to 2023. The increasing resistance of P. aeruginosa to carbapenems, as the last-resort antibiotics, is considered a global threat. Therefore, it requires new treatment approaches to fight against antimicrobial resistance and reduce the treatment failure. Moreover, applying the national and international surveillance programmes is necessary to control the emergence and spreading of carbapenem-resistant strains.

Data availability statement

The datasets supporting this study are included in the article, and excess information is available from the corresponding author upon request.

Author contribution

M.H.A.: Conceptualization, Formal analysis, Methodology, Resources, Supervision, Funding acquisition, Writing–review & editing. Z.F.A.: Data curation, Formal analysis, Methodology, Investigation, Writing – original draft. S.B.: Conceptualization, Formal analysis, Methodology.

Competing interests

The authors declare that they have no competing interests.

References

Terahara, F and Nishiura, H (2019) Carbapenem-resistant Pseudomonas aeruginosa and carbapenem use in Japan: An ecological study. Journal of International Medical Research 47, 4711–4722.10.1177/0300060519864181CrossRefGoogle ScholarPubMed
Abdeta, A, et al. (2023) Prevalence and trends of Carbapenem-resistant Pseudomonas aeruginosa and Acinetobacter species isolated from clinical specimens at the Ethiopian Public Health Institute, Addis Ababa, Ethiopia: A retrospective analysis. Infection and Drug Resistance, 1381–1390.10.2147/IDR.S403360CrossRefGoogle Scholar
Yin, R, et al. (2022) Treatment of Pseudomonas aeruginosa infectious biofilms: Challenges and strategies. Frontiers in Microbiology 3325.Google Scholar
Kaluba, CK, et al. (2021) Carbapenem resistance in Pseudomonas aeruginosa and Acinetobacter species at a large tertiary referral hospital in Lusaka, Zambia. Scientific African 13, e00908.10.1016/j.sciaf.2021.e00908CrossRefGoogle Scholar
Raman, G, et al. (2018) Risk factors for hospitalized patients with resistant or multidrug-resistant Pseudomonas aeruginosa infections: A systematic review and meta-analysis. Antimicrobial Resistance and Infection Control 7, 1–14.10.1186/s13756-018-0370-9CrossRefGoogle ScholarPubMed
Reynolds, D and Kollef, M (2021) The epidemiology and pathogenesis and treatment of Pseudomonas aeruginosa infections: An update. Drugs 81, 2117–2131.10.1007/s40265-021-01635-6CrossRefGoogle ScholarPubMed
Chatterjee, M, et al. (2016) Antibiotic resistance in Pseudomonas aeruginosa and alternative therapeutic options. International Journal of Medical Microbiology 306, 48–58.10.1016/j.ijmm.2015.11.004CrossRefGoogle ScholarPubMed
Horcajada, JP, et al. (2019) Epidemiology and treatment of multidrug-resistant and extensively drug-resistant Pseudomonas aeruginosa infections. Clinical Microbiology Reviews 32, 00019–00031. https://doi.org/10.1128/cmr.CrossRefGoogle ScholarPubMed
Kadri, SS, et al. (2018) 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. Clinical Infectious Diseases 67, 1803–1814.10.1093/cid/ciy378CrossRefGoogle ScholarPubMed
Karruli, A, et al. (2023) Evidence-based treatment of Pseudomonas aeruginosa infections: A critical reappraisal. Antibiotics 12, 399.10.3390/antibiotics12020399CrossRefGoogle ScholarPubMed
Carbapenems (2012) LiverTox: Clinical and Research Information on Drug-Induced Liver Injury. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney DiseasesGoogle Scholar
Tenover, FC, Nicolau, DP and Gill, CM (2022) Carbapenemase-producing Pseudomonas aeruginosa -an emerging challenge. Emerging Microbes & Infections 11, 811–814.10.1080/22221751.2022.2048972CrossRefGoogle ScholarPubMed
Wei, X, et al. (2023) Risk factors and outcomes of patients with Carbapenem-resistant Pseudomonas aeruginosa bloodstream infection. Infection & Drug Resistance 16, 337–346.10.2147/IDR.S396428CrossRefGoogle ScholarPubMed
Shrivastava, SR, Shrivastava, PS and Ramasamy, J (2018) World health organization releases global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. Journal of Medical Society 32, 76–77.10.4103/jms.jms_25_17CrossRefGoogle Scholar
Moher, D, et al. (2010) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery 8, 336–341.10.1016/j.ijsu.2010.02.007CrossRefGoogle ScholarPubMed
Kelley, GA and Kelley, KS (2012) Statistical models for meta-analysis: A brief tutorial. World Journal of Methodology 2, 27.10.5662/wjm.v2.i4.27CrossRefGoogle ScholarPubMed
Thompson, SG and Higgins, JP (2002) How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine 21, 1559–1573.10.1002/sim.1187CrossRefGoogle ScholarPubMed
Hafiz, TA, et al. (2023) Epidemiological, microbiological, and clinical characteristics of multi-resistant Pseudomonas aeruginosa isolates in king Fahad Medical City, Riyadh, Saudi Arabia. Tropical Medicine and Infectious Disease 8, 205.10.3390/tropicalmed8040205CrossRefGoogle Scholar
Liu, Y, et al. (2023) Antibiotic susceptibility pattern, risk factors, and prediction of carbapenem-resistant Pseudomonas aeruginosa in patients with nosocomial pneumonia. Heliyon 9.Google ScholarPubMed
Olalekan, A, et al. (2023) High incidence of carbapenemase-producing Pseudomonas aeruginosa clinical isolates from Lagos, Nigeria. JAC-Antimicrobial Resistance 5, dlad038.10.1093/jacamr/dlad038CrossRefGoogle ScholarPubMed
Coşkun, USŞ and Dagcioglu, Y (2023) Evaluation of toxin-antitoxin genes, antibiotic resistance, and virulence genes in Pseudomonas aeruginosa isolates. Revista Da Associacao Medica Brasileira 69, 51–55.10.1590/1806-9282.20220493CrossRefGoogle ScholarPubMed
Rahman, SPC, Shah, H and Sofiur, MM (2022) Phenotypic detection of MBL in Meropenem resistant P. Aeruginosa among clinical Isolatesin a tertiary care hospital. European journal of molecular & clinical medicine 9, 116–122.Google Scholar
Sambrano, H, et al. (2021) Prevalence of antibiotic resistance and virulent factors in nosocomial clinical isolates of Pseudomonas aeruginosa from Panamá. Brazilian Journal of Infectious Diseases 25.Google ScholarPubMed
Hassan, R, et al. (2021) Anti-biotic resistance pattern of bacteria causing hospital acquired infections in the new Mansoura general hospital. Egypt. Archives of Community Medicine 3, 16–21.Google Scholar
Negm, EM, et al. (2021) Antibiograms of intensive care units at an Egyptian tertiary care hospital. Egyptian Journal of Bronchology 15, 1–15.10.1186/s43168-021-00059-wCrossRefGoogle Scholar
Alhussain, FA, et al. (2021) Risk factors, antimicrobial susceptibility pattern and patient outcomes of Pseudomonas aeruginosa infection: A matched case-control study. Journal of Infection and Public Health 14, 152–157.10.1016/j.jiph.2020.11.010CrossRefGoogle ScholarPubMed
Gysin, M, et al. (2021) Antimicrobial susceptibility patterns of respiratory gram-negative bacterial isolates from COVID-19 patients in Switzerland. Annals of Clinical Microbiology and Antimicrobials 20, 1–10.10.1186/s12941-021-00468-1CrossRefGoogle ScholarPubMed
Vijayakumar, A, et al. (2021) Microbiological and antibiotic profile of osteomyelitis in tertiary care hospital. International Surgery Journal 8, 910–914.Google Scholar
McAulay, K, et al. (2021) Multidrug-resistant Pseudomonas aeruginosa in healthcare facilities in Port-Au-Prince, Haiti. Journal of Global Antimicrobial Resistance 25, 60–65.10.1016/j.jgar.2021.02.016CrossRefGoogle ScholarPubMed
Mnyambwa, NP, et al. (2021) Antibiotic susceptibility patterns of bacterial isolates from routine clinical specimens from referral hospitals in Tanzania: A prospective hospital-based observational study. Infection and Drug Resistance, 869–878.10.2147/IDR.S294575CrossRefGoogle ScholarPubMed
Mirzaei, B, et al. (2020) Prevalence of multi-drug resistant (MDR) and extensively drug-resistant (XDR) phenotypes of Pseudomonas aeruginosa and Acinetobacter baumannii isolated in clinical samples from northeast of Iran. BMC Research Notes 13, 1–6.10.1186/s13104-020-05224-wCrossRefGoogle ScholarPubMed
Kresken, M, et al. (2020) Dissemination of carbapenem-resistant Pseudomonas aeruginosa isolates and their susceptibilities to ceftolozane-tazobactam in Germany. International Journal of Antimicrobial Agents 55, 105959.10.1016/j.ijantimicag.2020.105959CrossRefGoogle ScholarPubMed
Karlowsky, JA, et al. (2020) In vitro activity of imipenem/relebactam against Enterobacteriaceae and Pseudomonas aeruginosa isolated from intraabdominal and urinary tract infection samples: SMART surveillance United States 2015–2017. Journal of Global Antimicrobial Resistance 21, 223–228.10.1016/j.jgar.2019.10.028CrossRefGoogle Scholar
Hassuna, NA, et al. (2020) Molecular epidemiology and mechanisms of high-level resistance to meropenem and imipenem in Pseudomonas aeruginosa. Infection and Drug Resistance, 285–293.10.2147/IDR.S233808CrossRefGoogle ScholarPubMed
Omer, THS, Mustafa, SAM and Mohamed, SOO (2020) Extended Spectrum β-lactamase-mediated resistance and Antibiogram of Pseudomonas aeruginosa isolates from patients attending two public hospitals in Khartoum, Sudan. International Journal of Microbiology 2020.10.1155/2020/2313504CrossRefGoogle ScholarPubMed
Farooq, L, et al. (2020) Antimicrobial susceptibility patterns of clinical isolates of Pseudomonas aeruginosa isolates from patients of respiratory tract infections in a tertiary care hospital, Karachi. European Journal of Experimental Biology 10, 1.Google Scholar
Wang, J, et al. (2020) Antimicrobial resistance in southern China: Results of prospective surveillance in Dongguan city, 2017. Journal of Hospital Infection 105, 188–196.10.1016/j.jhin.2020.03.029CrossRefGoogle ScholarPubMed
Basha, AM, El-Sherbiny, GM and Mabrouk, MI (2020) Phenotypic characterization of the Egyptian isolates “extensively drug-resistant Pseudomonas aeruginosa” and detection of their metallo-β-lactamases encoding genes. Bulletin of the National Research Centre 44, 1–11.10.1186/s42269-020-00350-8CrossRefGoogle Scholar
Saleem, S and Bokhari, H (2020) Resistance profile of genetically distinct clinical Pseudomonas aeruginosa isolates from public hospitals in Central Pakistan. Journal of Infection and Public Health 13, 598–605.10.1016/j.jiph.2019.08.019CrossRefGoogle ScholarPubMed
Zamudio, R, et al. (2019) Phylogenetic analysis of resistance to ceftazidime/avibactam, ceftolozane/tazobactam and carbapenems in piperacillin/tazobactam-resistant Pseudomonas aeruginosa from cystic fibrosis patients. International Journal of Antimicrobial Agents 53, 774–780.10.1016/j.ijantimicag.2019.02.022CrossRefGoogle ScholarPubMed
Zubair, K and Iregbu, K (2018) Resistance pattern and detection of metallobetalactamase genes in clinical isolates of Pseudomonas aeruginosa in a Central Nigeria tertiary hospital. Nigerian Journal of Clinical Practice 21, 176–182.Google Scholar
Oladipo, E, et al. (2018) Antimicrobial resistance pattern of clinical isolates of Pseudomonas aeruginosa and Escherichia coli on carbapenems. African Journal of Clinical and Experimental Microbiology 19.10.4314/ajcem.v19i3.1CrossRefGoogle Scholar
Babu, S (2018) Antibiotic susceptibility pattern of Pseudomonas aeruginosa isolates from wound infections in a tertiary care Centre in South Kerala, India. Indian journal of critical care medicine 3, 30.Google Scholar
Hussein, ZK and Shamkhi, IJ (2018) Detection of Bla-vim1gene in carbapenemresistant pseudomonas aeruginosa isolated from clinical samples in Wasit province hospitals. Basra journal of veterinary research 17, 30–44.10.23975/bjvetr.2018.172844CrossRefGoogle Scholar
Morales, S, et al. (2018) Detection of carbapenem resistance genes in Pseudomonas aeruginosa isolates with several phenotypic susceptibility profiles. Ces Medicina 32, 203–214.10.21615/cesmedicina.32.3.2CrossRefGoogle Scholar
Jiménez-Guerra, G, et al. (2018) Urinary tract infection by Acinetobacter baumannii and Pseudomonas aeruginosa: Evolution of antimicrobial resistance and therapeutic alternatives. Journal of Medical Microbiology, 790–797.10.1099/jmm.0.000742CrossRefGoogle ScholarPubMed
Zowawi, HM, et al. (2018) Identification of carbapenem-resistant Pseudomonas aeruginosa in selected hospitals of the Gulf cooperation council states: Dominance of high-risk clones in the region. Journal of Medical Microbiology 67, 846–853.10.1099/jmm.0.000730CrossRefGoogle ScholarPubMed
Badger-Emeka, LI, Emeka, PM and Quadri, S (2018) A five-year retrospective study of the antimicrobial susceptibility pattern of Pseudomonas aeruginosa ICU clinical isolates in Al-Ahsa, Saudi Arabia. Biomedical Research 29, 3856–3862.10.4066/biomedicalresearch.29-18-1154CrossRefGoogle Scholar
Rostami, S, et al. (2018) Investigating of four main carbapenem-resistance mechanisms in high-level carbapenem resistant Pseudomonas aeruginosa isolated from burn patients. Journal of the Chinese Medical Association 81, 127–132.10.1016/j.jcma.2017.08.016CrossRefGoogle ScholarPubMed
Singh-Moodley, A, et al. (2018) Laboratory based antimicrobial resistance surveillance for Pseudomonas aeruginosa blood isolates from South Africa. Journal of Infection in Developing Countries 12, 616–624.10.3855/jidc.9539CrossRefGoogle ScholarPubMed
Karlowsky, JA, et al. (2018) Activity of imipenem/relebactam against Pseudomonas aeruginosa with antimicrobial-resistant phenotypes from seven global regions: SMART 2015–2016. Journal of Global Antimicrobial Resistance 15, 140–147.10.1016/j.jgar.2018.07.012CrossRefGoogle ScholarPubMed
Zeb, A, et al. (2017) Antibiotic susceptibility pattrens of Pseudomonas aeruginosa in tertiary care hospital. Journal of Entomology and Zoology Studies 20, 50.Google Scholar
Mohamad, SM, et al. (2017) Detection of exotoxins and antimicrobial susceptibility pattern in clinical Pseudomonas aeruginosa isolates. Avicenna Journal of Clinical Microbiology and Infection 5, 36–40.10.34172/ajcmi.2018.07CrossRefGoogle Scholar
Negi, A, et al. (2017) Assessment of doripenem, meropenem, and imipenem against respiratory isolates of Pseudomonas aeroginosa in a tertiary care hospital of North India. Indian journal of critical care medicine 21, 703.10.4103/ijccm.IJCCM_341_17CrossRefGoogle Scholar
Lila, G, et al. (2017) The prevalence and resistance patterns of Pseudomonas aeruginosa in a tertiary care hospital in Kosovo. Infezioni in Medicina 25, 21–26.Google Scholar
Samad, A, et al. (2017) Antimicrobial susceptibility patterns of clinical isolates of Pseudomonas aeruginosa isolated from patients of respiratory tract infections in a tertiary care hospital, Peshawar. Pakistan Journal of Medical Sciences 33, 670.10.12669/pjms.333.12416CrossRefGoogle Scholar
Juayang, AC, et al. (2017) Five-year antimicrobial susceptibility of Pseudomonas aeruginosa from a local tertiary hospital in Bacolod City, Philippines. Tropical Medicine and Infectious Disease 2, 28.10.3390/tropicalmed2030028CrossRefGoogle ScholarPubMed
de Almeida, KdCF, et al. (2017) Molecular characterization of multidrug-resistant (MDR) Pseudomonas aeruginosa isolated in a burn center. Burns 43:137–143.10.1016/j.burns.2016.07.002CrossRefGoogle Scholar
Campana, EH, et al. (2017) Carbapenem-resistant and cephalosporin-susceptible: A worrisome phenotype among Pseudomonas aeruginosa clinical isolates in Brazil. Brazilian Journal of Infectious Diseases 21, 57–62.10.1016/j.bjid.2016.10.008CrossRefGoogle ScholarPubMed
Lakum, S, et al. (2016) Antibiotic susceptibility pattern of Pseudomonas aeruginosa at the tertiary care center, Dhiraj hospital, Piparia, Gujarat. International Archives of Integrated Medicine 3, 133–137.Google Scholar
Bhat, V, et al. (2016) Bacteriological profile and antibiotic susceptibility patterns of clinical isolates in a tertiary care cancer center. Indian Journal of Medical and Paediatric Oncology 37, 20–24.Google Scholar
Lin, K-Y, et al. (2016) Carbapenem-resistant Pseudomonas aeruginosa in Taiwan: Prevalence, risk factors, and impact on outcome of infections. Journal of Microbiology, Immunology and Infection 49, 52–59.10.1016/j.jmii.2014.01.005CrossRefGoogle ScholarPubMed
Malkoçoğlu, G, et al. (2017) VIM-1, VIM-2, and GES-5 carbapenemases among Pseudomonas aeruginosa isolates at a tertiary hospital in Istanbul, Turkey. Microbial Drug Resistance 23, 328–334.10.1089/mdr.2016.0012CrossRefGoogle Scholar
Radan, M, et al. (2016) Emerging carbapenem-resistant Pseudomonas aeruginosa isolates carrying blaIMP among burn patients in Isfahan, Iran. Archives of Trauma Research 5.10.5812/atr.33664CrossRefGoogle ScholarPubMed
Qadeer, A, et al. (2016) Antibiogram of medical intensive care unit at tertiary care hospital setting of Pakistan. Cureus 8, e809.Google ScholarPubMed
Shah, DA, Wasim, S and Abdullah, FE (2015) Antibiotic resistance pattern of Pseudomonas aeruginosa isolated from urine samples of urinary tract infections patients in Karachi, Pakistan. Pakistan Journal of Medical Sciences 31, 341.Google ScholarPubMed
Vaez, H, et al. (2015) Clonal relatedness among imipenem-resistant Pseudomonas aeruginosa isolated from ICU-hospitalized patients. Critical Care Research and Practice 2015.10.1155/2015/983207CrossRefGoogle ScholarPubMed
Kılınç, Ç, et al. (2015) Antibacterial resistance in Pseudomonas aeruginosa strains isolated from various clinical samples. Respiration 104, 31–38.Google Scholar
Sutherland, CA and Nicolau, DP (2015) Susceptibility profile of ceftolozane/tazobactam and other parenteral antimicrobials against Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa from US hospitals. Clinical Therapeutics 37, 1564–1571.10.1016/j.clinthera.2015.05.501CrossRefGoogle ScholarPubMed
Neyestanaki, DK, et al. (2014) Determination of extended spectrum beta-lactamases, metallo-beta-lactamases and AmpC-beta-lactamases among carbapenem resistant Pseudomonas aeruginosa isolated from burn patients. Burns 40, 1556–1561.10.1016/j.burns.2014.02.010CrossRefGoogle ScholarPubMed
Dash, M, et al. (2014) Antimicrobial resistance pattern of Pseudomonas aeruginosa isolated from various clinical samples in a tertiary care hospital, South Odisha, India. Saudi Journal for Health Sciences 3, 15–19.10.4103/2278-0521.130200CrossRefGoogle Scholar
Ranjan, S, Banashankari, G and Babu, PS (2014) Comparison of epidemiological and antibiotic susceptibility pattern of metallo-beta-lactamase-positive and metallo-beta-lactamase-negative strains of Pseudomonas aeruginosa. Journal of Laboratory Physicians 6, 109–113.Google ScholarPubMed
Corehtash, ZG, et al. (2015) Biofilm formation and virulence factors among Pseudomonas aeruginosa isolated from burn patients. Jundishapur Journal of Microbiology 8.Google Scholar
Aghamiri, S, et al. (2014) Antibiotic resistance pattern and evaluation of metallo-beta lactamase genes including Bla-IMP and Bla-VIM types in Pseudomonas aeruginosa isolated from patients in Tehran hospitals. International Scholarly Research Notices 2014.Google ScholarPubMed
Shanthi, J, et al. (2013) Mechanism of drug resistance, characterization of plasmid-borne determinants and transformation study in P. Aeruginosa from burn and ICU units-its susceptibility pattern. Burns 39, 643–649.10.1016/j.burns.2012.08.012CrossRefGoogle Scholar
Bessa, LJ, et al. (2015) Bacterial isolates from infected wounds and their antibiotic susceptibility pattern: Some remarks about wound infection. International Wound Journal 12, 47–52.10.1111/iwj.12049CrossRefGoogle ScholarPubMed
Agrawal, A, et al. (2013) Microbiological profile and their antimicrobial sensitivity pattern in patients of otitis media with ear discharge. Indian Journal of Otology 19, 5–8.10.4103/0971-7749.108149CrossRefGoogle Scholar
Anil, C and Shahid, RM (2013) Antimicrobial susceptibility patterns of Pseudomonas aeruginosa clinical isolates at a tertiary care hospital in Kathmandu, Nepal. Asian Journal of Pharmaceutical and Clinical Research 6, 235–238.Google Scholar
Mohanty, S, et al. (2013) Phenotypic characterization and colistin susceptibilities of carbapenem-resistant of Pseudomonas aeruginosa and Acinetobacter spp. Journal of Infection in Developing Countries 7, 880–887.10.3855/jidc.2924CrossRefGoogle ScholarPubMed
Somayeh, M-G and Eftekhar, F (2013) Assessment of carbapenem susceptibility and multidrug-resistance in Pseudomonas aeruginosa burn isolates in Tehran. Jundishapur Journal of Microbiology 6, 162–165.Google Scholar
Somily, AM, et al. (2012) Antimicrobial susceptibility patterns of multidrug-resistant Pseudomonas aeruginosa and Acinetobacter baumannii against carbapenems, colistin, and tigecycline. Saudi Medical Journal 33, 750–755.Google ScholarPubMed
Khuntayaporn, P, et al. (2012) Prevalence and genotypic relatedness of carbapenem resistance among multidrug-resistant P. Aeruginosa in tertiary hospitals across Thailand. Annals of Clinical Microbiology and Antimicrobials 11, 1–7.10.1186/1476-0711-11-25CrossRefGoogle ScholarPubMed
Fatima, A, et al. (2012) Antimicrobial susceptibility pattern of clinical isolates of Pseudomonas aeruginosa isolated from patients of lower respiratory tract infections. Springerplus 1, 1–4.10.1186/2193-1801-1-70CrossRefGoogle ScholarPubMed
Yousefi, S, et al. (2010) Class 1 integron and imipenem resistance in clinical isolates of Pseudomonas aeruginosa: Prevalence and antibiotic susceptibility. Iranian Journal of Microbiology 2, 115.Google ScholarPubMed
Ullah, F, Malik, SA and Ahmed, J (2009) Antimicrobial susceptibility and ESBL prevalence in Pseudomonas aeruginosa isolated from burn patients in the north west of Pakistan. Burns 35, 1020–1025.10.1016/j.burns.2009.01.005CrossRefGoogle ScholarPubMed
Pathmanathan, SG, Samat, NA and Mohamed, R (2009) Antimicrobial susceptibility of clinical isolates of Pseudomonas aeruginosa from a Malaysian hospital. Malaysian Journal of Medical Sciences 16, 27.Google ScholarPubMed
Mansoor, T, et al. (2009) Pseudomonas aeruginosa in chronic suppurative otitis media: Sensitivity spectrum against various antibiotics in Karachi. Journal of Ayub Medical College, Abbottabad 21, 120–123.Google ScholarPubMed
Goel, N, et al. (2009) Antibiotic sensitivity pattern of gram negative bacilli isolated from the lower respiratory tract of ventilated patients in the intensive care unit. ndian journal of critical care medicine 13, 148.Google ScholarPubMed
Gad, GF, El-Domany, RA and Ashour, HM (2008) Antimicrobial susceptibility profile of Pseudomonas aeruginosa isolates in Egypt. Journal of Urology 180, 176–181.10.1016/j.juro.2008.03.081CrossRefGoogle ScholarPubMed
Javiya, VA, et al. (2008) Antibiotic susceptibility patterns of Pseudomonas aeruginosa at a tertiary care hospital in Gujarat, India. Indian Journal Of Pharmacology 40, 230.Google Scholar
Rahbar, M, et al. (2008) Carbapenem resistance in gram-negative bacilli isolates in an Iranian 1000-bed tertiary hospital. Pakistan Journal of Medical Sciences 24, 537–540.Google Scholar
Brink, A, et al. (2007) Antimicrobial susceptibility profile of selected bacteraemic pathogens from private institutions in South Africa. South African Medical Journal 97, 273–279.Google ScholarPubMed
Al-Tawfiq, JA (2007) Occurrence and antimicrobial resistance pattern of inpatient and outpatient isolates of Pseudomonas aeruginosa in a Saudi Arabian hospital: 1998–2003. International Journal of Infectious Diseases 11, 109–114.10.1016/j.ijid.2005.11.004CrossRefGoogle Scholar
Manno, G, et al. (2005) Antimicrobial use and Pseudomonas aeruginosa susceptibility profile in a cystic fibrosis Centre. International Journal of Antimicrobial Agents 25, 193–197.10.1016/j.ijantimicag.2004.11.009CrossRefGoogle Scholar
Savaş, L, et al. (2005) The prevalence and resistance patterns of Pseudomonas aeruginosa in intensive care units in a university hospital. Turkish Journal of Medical Sciences 35, 317–322.Google Scholar
Brown, PD and Izundu, A (2004) Antibiotic resistance in clinical isolates of Pseudomonas aeruginosa in Jamaica. Revista Panamericana de Salud Publica 16, 125–130.10.1590/S1020-49892004000800008CrossRefGoogle ScholarPubMed
Van Eldere, J (2003) Multicentre surveillance of Pseudomonas aeruginosa susceptibility patterns in nosocomial infections. Journal of Antimicrobial Chemotherapy 51, 347–352.10.1093/jac/dkg102CrossRefGoogle ScholarPubMed
Andrade, SS, et al. (2003) Increasing prevalence of antimicrobial resistance among Pseudomonas aeruginosa isolates in Latin American medical centres: 5 year report of the SENTRY antimicrobial surveillance program (1997–2001). Journal of Antimicrobial Chemotherapy 52, 140–141.10.1093/jac/dkg270CrossRefGoogle ScholarPubMed
Gençer, S, et al. (2002) Susceptibility patterns and cross resistances of antibiotics against Pseudomonas aeruginosa in a teaching hospital of Turkey. Annals of Clinical Microbiology and Antimicrobials 1, 1–4.10.1186/1476-0711-1-2CrossRefGoogle Scholar
El Amari, EB, et al. (2001) Influence of previous exposure to antibiotic therapy on the susceptibility pattern of Pseudomonas aeruginosa bacteremic isolates. Clinical Infectious Diseases 33, 1859–1864.10.1086/324346CrossRefGoogle Scholar
Henwood, CJ, et al. (2001) Antimicrobial susceptibility of Pseudomonas aeruginosa: Results of a UK survey and evaluation of the British Society for Antimicrobial Chemotherapy disc susceptibility test. Journal of Antimicrobial Chemotherapy 47, 789–799.10.1093/jac/47.6.789CrossRefGoogle ScholarPubMed
Fluit, A, et al. (2000) Antimicrobial resistance in European isolates of Pseudomonas aeruginosa. European Journal of Clinical Microbiology & Infectious Diseases 19, 370–374.10.1007/s100960050497CrossRefGoogle ScholarPubMed
Pfaller, MA, et al. (1998) Bacterial pathogens isolated from patients with bloodstream infection: Frequencies of occurrence and antimicrobial susceptibility patterns from the SENTRY antimicrobial surveillance program (United States and Canada, 1997). Antimicrobial Agents and Chemotherapy 42, 1762–1770.10.1128/AAC.42.7.1762CrossRefGoogle Scholar
Iaconis, JP, et al. (1997) Comparison of antibacterial activities of meropenem and six other antimicrobials against Pseudomonas aeruginosa isolates from north American studies and clinical trials. Clinical Infectious Diseases 24, S191–S196.10.1093/clinids/24.Supplement_2.S191CrossRefGoogle ScholarPubMed
Murray, CJL, et al. (2022) Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. The Lancet 399, 629–655.10.1016/S0140-6736(21)02724-0CrossRefGoogle Scholar
Zequinão, T, et al. (2020) Carbapenem stewardship with ertapenem and antimicrobial resistance-a scoping review. Revista da Sociedade Brasileira de Medicina Tropical 53, e20200413.10.1590/0037-8682-0413-2020CrossRefGoogle ScholarPubMed
Aguilar, GR, et al. (2023) The burden of antimicrobial resistance in the Americas in 2019: A cross-country systematic analysis. The Lancet Regional Health – Americas 25.10.1016/j.lana.2023.100561CrossRefGoogle Scholar
Burke, RM, et al. (2021) Community-based active case-finding interventions for tuberculosis: A systematic review. The Lancet Public Health 6, e283–e299.10.1016/S2468-2667(21)00033-5CrossRefGoogle ScholarPubMed
W.H. Organization (2022) Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report 2022. Geneva, Switzerland: World Health OrganizationGoogle Scholar
W.H. Organization (2024) WHO Bacterial Priority Pathogens List, 2024. Geneva, Switzerland: World Health OrganizationGoogle Scholar
Umemura, Y, et al. (2023) Efficacy of Carbapenems compared with Noncarbapenem broad-Spectrum Beta-lactam antibiotics as initial antibiotic therapy against sepsis: A Nationwide observational study. Critical Care Medicine 51, 1210–1221.10.1097/CCM.0000000000005932CrossRefGoogle ScholarPubMed
C.a.L.S. Institute (2011) Performance Standards for Antimicrobial Susceptibility Testing: Twenty-First Informational Supplement CLSI document M100-S21. Wayne, PA: Clinical and Laboratory Standards InstituteGoogle Scholar
C.a.L.S. Institute (2022) Performance Standards for Antimicrobial Susceptibility Testing CLSI document M100-Ed32. Wayne, PA: Clinical and Laboratory Standards InstituteGoogle Scholar
Li, Y, Yang, X and Zhao, W (2017) Emerging microtechnologies and automated Systems for Rapid Bacterial Identification and Antibiotic Susceptibility Testing. SLAS Technology: Translating Life Sciences Innovation 22, 585–608.10.1177/2472630317727519CrossRefGoogle ScholarPubMed
Benkova, M, Soukup, O and Marek, J (2020) Antimicrobial susceptibility testing: Currently used methods and devices and the near future in clinical practice. Journal of Applied Microbiology 129, 806–822.10.1111/jam.14704CrossRefGoogle ScholarPubMed
Dorotkiewicz-Jach, A, et al. (2015) Modern therapeutic approaches against Pseudomonas aeruginosa infections. Current Medicinal Chemistry 22, 1642–1664.10.2174/0929867322666150417122531CrossRefGoogle ScholarPubMed
Figure 0

Figure 1. Flow chart of the literature search, systematic review and study selection.

Figure 1

Table 1. Studies included in meta-analysis after final evaluation

Figure 2

Figure 2. Forest plot of the meta-analysis of resistance rate of P. aeruginosa to imipenem.

Figure 3

Figure 3. Forest plot of the meta-analysis of resistance rate of P. aeruginosa to meropenem.

Figure 4

Figure 4. Forest plots of the meta-analysis of resistance rate to ertapenem (a) and doripenem (b) for P. aeruginosa.

Figure 5

Table 2. Meta-analysis results for resistance rate of Pseudomonas aeruginosa in included studies

Figure 6

Figure 5. The resistance rates of P. aeruginosa to meropenem (a) and imipenem (b) during the years 1997 to 2023. Dashed lines represent 95% confidence intervals (CI: 0.2491–0.8040).

Figure 7

Figure 6. Funnel plots of the meta-analysis for the resistance rate of P. aeruginosa isolates to carbapenems: imipenem (a), meropenem (b), ertapenem (c), and doripenem (d).