https://orcid.org/0000-0003-1243-3164
Figures
Abstract
Background
The emergence of antibiotic-resistant Elizabethkingia strains poses a significant challenge in clinical settings. This systematic review and meta-analysis provide a comprehensive assessment of the prevalence of antibiotic resistance among Elizabethkingia isolates across different regions worldwide.
Methods
A systematic literature search was conducted across PubMed, Embase, Web of Science, and Scopus from 1998 to January 9, 2024, using predefined search strategies. Eligible studies reporting antibiotic resistance in Elizabethkingia were included. A random-effects model was applied to estimate resistance proportions and assess heterogeneity. Additional analyses, including meta-regression, subgroup evaluations, and assessments of outliers and influential studies, were performed to explore resistance trends and evaluate publication bias. Study quality was assessed using the Joanna Briggs Institute Checklist, and all statistical analyses were conducted using R with the metafor package.
Results
A total of 1,016 articles were identified, of which 34 studies (47 reports) met the inclusion criteria. The pooled analysis indicated high resistance to ceftazidime (88.5% of isolates, 95% CI: 21.1%–99.5%) with no significant heterogeneity. Resistance to rifampin was 12.5% (95% CI: 5.9%–24.7%), showing substantial heterogeneity, while ciprofloxacin resistance was 27.9% (95% CI: 13.8%–48.4%) with considerable heterogeneity. Among specific antibiotics, cefotaxime resistance was 96.1% (220 isolates), meropenem resistance was 92.4% (353 isolates), and gentamicin resistance was 77.7% (356 isolates). Additionally, sulfamethoxazole resistance was 46.1% (176 out of 360 isolates, 95% CI: 23.5%–70.4%), displaying significant heterogeneity.
Conclusion
This study highlights the widespread antibiotic resistance in Elizabethkingia, underscoring the urgent need for targeted treatment strategies and enhanced surveillance programs. The findings emphasize the importance of monitoring local resistance patterns to guide clinical decision-making. Future research should focus on elucidating resistance mechanisms and genetic diversity to develop practical therapeutic approaches and mitigate the global health impact of Elizabethkingia infections.
Citation: Jasim SA, Mohammed JS, Roopashree R, Alshahrani MY, Sharma A, Sharma A, et al. (2025) Unraveling the global landscape of Elizabethkingia antibiotic resistance: A systematic review and meta-analysis. PLoS One 20(5): e0323313. https://doi.org/10.1371/journal.pone.0323313
Editor: Ali Amanati, Shiraz University of Medical Sciences, IRAN, ISLAMIC REPUBLIC OF
Received: December 7, 2024; Accepted: April 4, 2025; Published: May 30, 2025
Copyright: © 2025 Jasim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files. I confirm that the submission contains all raw data required to replicate the results of the study.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
The emergence of multidrug-resistant (MDR) Elizabethkingia species presents a significant global health concern, particularly in immunocompromised patients [1]. Elizabethkingia, a Gram-negative bacterium, is known to cause severe infections such as pneumonia, bacteremia, neonatal meningitis, and nosocomial outbreaks [2,3]. Since its discovery in 1959 by Elizabeth O. King, the genus has undergone multiple taxonomic revisions, with recent advancements in whole-genome sequencing refining its classification into eight species [4].
Antibiotic resistance in Elizabethkingia species, especially E. anophelis and E. meningoseptica, presents significant clinical challenges due to various resistance mechanisms [5]. These include the production of beta-lactamases, which degrade beta-lactam antibiotics like carbapenems and cephalosporins, and efflux pumps, such as the AcrAB system, to expel antibiotics like fluoroquinolones [6]. The genetic diversity among Elizabethkingia strains further complicates treatment, as different strains exhibit varying levels of susceptibility [7]. Additionally, the species’ ability to adapt to environmental reservoirs and acquire resistance genes from other microorganisms enhances their survival in clinical settings, particularly among immunocompromised patients. Together, these factors make managing infections caused by Elizabethkingia highly challenging [8]. Genetic diversity among strains further complicates treatment, with variations in susceptibility patterns and the acquisition of resistance genes enhancing their persistence in clinical settings [7,8]. Despite standardized Minimum Inhibitory Concentration (MIC) breakpoints, resistance is commonly assessed using Clinical and Laboratory Standards Institute (CLSI) M100 guidelines and European Committee on Antimicrobial Susceptibility Testing (EUCAST) PK–PD non-species breakpoints [9,10].
Recent outbreaks, such as the 2015–2016 Wisconsin cluster, have underscored Elizabethkingia’s adaptability and virulence, with Rapid genomic changes contributing to resistance and pathogenicity [11–13]. Intrinsic resistance mechanisms, including beta-lactamases and topoisomer modifications, further complicate antimicrobial therapy, highlighting the need for enhanced surveillance and antimicrobial stewardship [14–17]. The economic and clinical burden of Elizabethkingia infections, particularly in hospital settings, necessitates improved diagnostic strategies and infection control measures [18–21].
This systematic review and meta-analysis aim to comprehensively assess global Elizabethkingia resistance patterns, identify trends in antimicrobial susceptibility, and explore regional variations. By including studies from 1998 to 2023, we ensure a broad temporal analysis of resistance trends. Additionally, we assess the impact of testing methodologies on reported resistance rates, aiming to bridge knowledge gaps and support informed clinical decision-making in managing Elizabethkingia infections.
2. Methods
This systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [22] guidelines and were registered with PROSPERO (ID: CRD42025645625, https://www.crd.york.ac.uk/PROSPERO/view/CRD42025645625).
The study aimed to evaluate antibiotic resistance patterns in Elizabethkingia species across various geographic regions.
2.1. Eligibility criteria
2.1.1. Inclusion criteria.
Study design and scope: Eligible studies included those that specifically examined resistance patterns in Elizabethkingia. This encompassed various study designs, such as observational studies (both prospective and retrospective), experimental research, and comprehensive surveillance reports, all of which supplied empirical data on resistance rates. Outcome Measures: The main criterion for inclusion was reporting the proportion of resistance to Elizabethkingia. Studies were expected to provide quantitative data on resistance rates, ideally defined according to recognized standards such as the CLSI and EUCAST guidelines.
Sample size and population: A minimum sample size is required for inclusion. Studies that examined Elizabethkingia isolates from any clinical or veterinary source were eligible, reflecting the pathogen’s relevance across different environments.
Publication date: The studies’ publication dates were not restricted to ensure a comprehensive analysis, allowing for the inclusion of historical data alongside contemporary findings.
2.1.2. Exclusion criteria.
Study design limitations: Single-arm studies were excluded because they do not provide complete data, essential for a nuanced understanding of resistance patterns.
Incomplete data: To maintain the integrity and reliability of the meta-analysis, studies needing more specific data on the proportion of Elizabethkingia resistance or those with unclear methodologies were excluded.
Review articles and editorials: To focus on original research data, narrative reviews, systematic reviews, meta-analyses, editorials, and opinion pieces were excluded. However, relevant insights from these sources were considered for qualitative contextualization.
Case reports and case series: Given their limited scope and potential for selection bias, the quantitative meta-analysis did not include case reports and small case series. However, they were noted for qualitative insights where applicable.
Duplicate studies: To avoid data redundancy and potential bias, studies reporting duplicated or subsets of data already covered in other publications were excluded.
2.2. Information sources and search strategy
From 1998 to January 9, 2024, a systematic literature search was conducted in Scopus, PubMed, Web of Science, and EMBASE. The search strategy included keywords such as Elizabethkingia
2.3. Study selection and data extraction
Duplicate records were removed using EndNote (version 21). Two independent reviewers screened titles, abstracts, and full texts based on predefined inclusion criteria. A third reviewer resolved discrepancies. Extracted data included study characteristics (authors, publication year, country, sample size), antibiotic susceptibility testing methods, and resistance rates.
2.4. Antibiotic breakpoints and cut-off values
This meta-analysis classified Elizabethkingia strains as resistant or sensitive based on the MIC values and disk diffusion (DD) zone diameters reported in the included studies. Since no officially defined breakpoints for Elizabethkingia exist in CLSI or EUCAST guidelines, the breakpoints were derived from commonly accepted guidelines for similar Gram-negative bacteria such as Pseudomonas aeruginosa, Enterobacteriaceae, and other related pathogens.
2.5. Risk of bias assessment
Study quality was assessed using the Joanna Briggs Institute (JBI) Checklist [23]. Two independent reviewers evaluated study reliability, and disagreements were resolved through discussion.
2.6. Statistical analysis
To estimate the pooled prevalence of antibiotic resistance, a random-effects model was applied, incorporating both within-study and between-study variability. Statistical analyses were performed using R (version 4.2.1) and the metafor package (version 3.8.1) [24–31]. Heterogeneity across studies was assessed using Cochran’s Q-test and the I² statistic, where a significant Q-test (p < 0.05) indicated heterogeneity, and I² quantified the proportion of total variation due to differences between studies (P2). Statistical significance was determined using different p-values for various comparisons: P1 represents the p-value for the difference from a zero-resistance rate, P2 denotes the p-value for heterogeneity between reports. Subgroup analyses explored variations in resistance proportions across geographic regions, study years, species, and susceptibility testing methods, with p-values for these comparisons reported as P3. Meta-regression analysis was conducted to examine temporal trends in resistance rates. Publication bias was assessed using rank correlation (Begg’s test) and regression analysis (Egger’s test), with funnel plot asymmetry examined to detect bias. The fill-and-trim method was applied to adjust for potential missing studies. Sensitivity analyses were performed by identifying and excluding influential studies through Studentized residuals and Cook’s distance to ensure the stability of pooled estimates.
2.7. Assessment of reporting bias and certainty
To evaluate potential reporting bias, we employed rank correlation and Egger’s regression tests to detect funnel plot asymmetry. Additionally, we applied the Fail-Safe N and Trim-and-Fill methods to mitigate the impact of publication bias and enhance the robustness of our findings. These complementary approaches further strengthened the reliability and credibility of our conclusions.
3. Results
3.1. Study selection
A total of 1,016 studies were retrieved from Scopus, PubMed, EMBASE, and Web of Science. After removing 512 duplicates, 119 non-relevant articles and non-English publications were excluded. Following the full-text screening, 34 eligible studies were included in the final analysis [32–65]. PRISMA flowchart illustrates the selection process (Fig 1 and S1 Table in S1 File).
3.2. Study characteristics
Between 1998 and 2023, the included studies were conducted in seven countries, including Asia, Africa, Europe, North America, South America, and Oceania, covering all four continents and studies spanning multiple continents. The studies employed a variety of antimicrobial susceptibility testing (AST) methods and followed different guidelines. Prevalence of Antibiotics resistance
3.3. Prevalence of antibiotic resistance
The pooled antibiotic resistance rates were estimated using a random-effects model, with heterogeneity assessed through I² and Q-tests.
3.3.1. Prevalence of ceftazidime resistance.
A total of 186 isolates from six studies were analyzed. The estimated resistance proportion was 0.885 (95% CI, 0.211–0.995), with significant heterogeneity (I² = 90.91%, p < 0.001). The fill-and-trim method adjusted the estimate to 0.703 (95% CI, 0.144–0.971). One potential outlier was identified [66], and exclusion resulted in a revised estimate of 0.703 (95% CI, 0.144–0.971) (Table 1). A forest plot showing the observed outcomes and the estimate based on the random effects model is shown in Fig 2. Funnel plot asymmetry was suggested by the rank correlation test (p = 0.017) but not by the regression test (p = 0.117) (Fig 3).
These plots visualize the relationship between study precision and effect estimates for various antibiotics. The symmetrical distribution around the central vertical line indicates minimal publication bias. To address potential asymmetry, the trim-and-fill method was applied. This method involves imputing studies for symmetry, represented by white circles. The adjusted resistance proportions post-Trim and Fill are as follows: Ceftazidime (0.703, 95%CI 0.144-0.971), Rifampin (0.185, 95%CI 0.095-0.330), Ciprofloxacin (0.372, 95%CI 0.203-0.580), Cefotaxime (0.968, 95%CI 0.934-0.985), Meropenem (0.880, 95%CI 0.753-0.946), and Gentamicin (0.557, 95%CI 0.327-0.764).
3.3.2. Prevalence of cefotaxime resistance.
Among 220 isolates across eight studies, the estimated resistance proportion was 0.961 (95% CI, 0.916–0.983), with no significant heterogeneity (I² = 0.00%, p = 0.789). The fill-and-trim method adjusted the estimate slightly to 0.968 (95% CI, 0.934–0.985). No outliers or overly influential studies were detected (Cook’s distance). Funnel plot asymmetry was indicated by the rank correlation test (p = 0.031) but not the regression test (p = 0.504).
3.3.3. Prevalence of meropenem resistance.
A total of 353 isolates from 12 studies were analyzed, yielding a resistance proportion of 0.924 (95% CI, 0.817–0.970). Significant heterogeneity was observed (I² = 73.44%, p < 0.001). The fill-and-trim method adjusted the estimate to 0.880 (95% CI, 0.753–0.946). One study [67] was identified as a potential outlier. Upon exclusion, the proportion remained at 0.880 (95% CI, 0.753–0.946). There was no evidence of funnel plot asymmetry (p = 0.879 and p = 0.662, respectively).
3.3.4. Prevalence of gentamicin resistance.
Among 356 isolates across 12 studies, the estimated resistance proportion was 0.777 (95% CI, 0.577–0.899) with significant heterogeneity (I² = 85.33%, p < 0.001). The fill-and-trim method adjusted the estimate to 0.557 (95% CI, 0.327–0.764). No outliers were detected. The regression test suggested funnel plot asymmetry (p = 0.002), while the rank correlation test did not (p = 0.191).
3.3.5. Prevalence of sulfamethoxazole resistance.
From 360 isolates across 13 studies, the estimated resistance proportion was 0.461 (95% CI, 0.235–0.704), with significant heterogeneity (I² = 89.02%, p < 0.001). The fill-and-trim method adjusted the estimate to 0.537 (95% CI, 0.286–0.771). No outliers were detected. The rank correlation test (p = 0.765) or the regression test (p = 0.428) did not indicate Funnel plot asymmetry.
3.4. Summary of small-scale studies on Elizabethkingia
Smaller studies provided additional insights into the clinical impact of Elizabethkingia infections, resistance mechanisms, and treatment outcomes. Despite their limited sample sizes, these studies contribute valuable context to the broader resistance patterns identified. A summary of key findings is provided in the S2 Table in S1 File.
3.5. Subgroup and temporal analysis
Resistance rates for cefotaxime, ceftazidime, meropenem, and gentamicin was consistently high across studies. However, no significant differences were found across subgroups based on study year, region, species, or testing method (P3 > 0.05). The only notable variation was in sulfamethoxazole resistance, which ranged from 3.7% in Taiwan to 89.2% in India (p < 0.001) (S3 Table in S1 File).
Temporal Analysis showed no statistical differences in the resistance rates over time (S1 Fig).
4. Discussion
This systematic review and meta-analysis aimed to elucidate antibiotic resistance patterns in Elizabethkingia, a genus of increasing clinical importance. Following a thorough search of major databases (Scopus, PubMed, EMBASE, and Web of Science), we identified 1,016 studies, from which 34 met our inclusion criteria. These studies, published between 1998 and 2023, spanned seven countries across four continents, providing a global perspective on Elizabethkingia resistance.
The high prevalence of Ceftazidime resistance (88.5%, 95% CI: 21.1%–99.5%) in Elizabethkingia is alarming, especially given its role as a last-resort antibiotic for many gram-negative infections. Our analysis revealed a consistent resistance pattern, as indicated by the lack of significant heterogeneity among studies, suggesting a robust and entrenched resistance mechanism within the genus. However, as reflected by the broad 95% confidence interval, the strikingly high resistance rate shows a high degree of uncertainty in the pooled estimate. This suggests that the true resistance rate may be overestimated due to variability in study sample sizes, methodologies, and reporting practices. Despite this uncertainty, the lack of significant heterogeneity still points to an underlying resistance mechanism within the Elizabethkingia genus.
The potential outliers identified in the analysis highlight the need for cautious interpretation and consideration of individual study contexts. This high resistance rate underscores the urgent need for alternative treatment strategies and ongoing surveillance of antibiotic resistance. Nevertheless, further large-scale studies are needed to refine this estimate and confirm its clinical implications.
The high resistance rate to cefotaxime (96.1%) and meropenem (92.4%) further underscores the growing challenge in treating Elizabethkingia infections, particularly in the context of multi-drug-resistant (MDR) strains.
Interestingly, rifampin demonstrated a relatively lower resistance rate (12.5%), but the significant heterogeneity suggests regional variations or differing resistance mechanisms. Similarly, ciprofloxacin resistance was found in 27.9% of isolates, with notable variability across studies, suggesting that resistance mechanisms may vary by region or strain type. Gentamicin resistance (77.7%) also demonstrated high prevalence, though significant heterogeneity was observed. Differences may influence this variability in local antibiotic usage practices or strain genetic diversity. Sulfamethoxazole resistance was found in 46.1% of isolates, with heterogeneity suggesting that local factors, such as antibiotic use patterns and strain variability, contribute to resistance.
Our subgroup and analysis across time provide valuable insights into the complex and evolving landscape of antibiotic resistance in Elizabethkingia spp. The high resistance rates observed to critical antibiotics such as Cefotaxime, Ceftazidime, Meropenem, and Gentamicin are of significant concern, particularly in clinical settings where these antibiotics are commonly used to treat infections caused by Gram-negative bacteria. These findings highlight the urgent need for alternative treatment strategies and the development of new therapeutic options. However, no statistical differences were noted in the resistance rates over time. This lack of statistical significance should not be interpreted as evidence of true uniformity in resistance patterns. Several factors may contribute to this outcome. First, small sample sizes and underpowering of the analysis could mask real differences in resistance rates across time periods. The inclusion of studies with varying methodologies, including differences in study design, geographic scope, and sample populations, may also obscure trends.
Additionally, variations in how antibiotic resistance data are reported, including differences in the reporting thresholds and methods for susceptibility testing, could introduce inconsistencies that affect the ability to detect temporal changes. Another potential reason for the lack of statistical significance is the aggregated nature of the data in this meta-analysis, which combines diverse regions, species, and methods. This broad aggregation may dilute trends that exist within more specific subgroups, such as particular Elizabethkingia species or regional populations, where resistance patterns may vary more significantly. Furthermore, the relatively short duration of some studies may not provide enough time to observe significant shifts in resistance patterns, particularly if resistance development is a gradual process influenced by long-term factors such as selective pressure from antibiotics or evolving environmental conditions.
Finally, the lack of observed variation may also reflect the stability of resistance patterns in Elizabethkingia, suggesting that resistance mechanisms may be well-established and remain consistent over time. While this stability could be seen as positive in predictability for clinical treatment, it also highlights the persistent nature of antibiotic resistance in Elizabethkingia spp., which is difficult to overcome without new therapeutic strategies. Our subgroup analyses revealed key insights into regional and temporal resistance trends. Notably, the geographical variation in sulfamethoxazole resistance—high in India (89.2%) and low in Taiwan (3.7%)—emphasizes the importance of region-specific resistance data and suggests that local healthcare practices and strain diversity play a major role in resistance development. Additionally, meta-regression analysis did not reveal a clear temporal trend in resistance, suggesting that although resistance remains high, it has not worsened significantly over time. This stability may reflect the entrenched nature of resistance mechanisms across Elizabethkingia strains.
Whole-genome sequencing (WGS) has provided valuable insights into the antibiotic resistance mechanisms and global transmission potential of Elizabethkingia spp. Several studies have characterized the genomic diversity of Elizabethkingia, revealing a wide array of antibiotic resistance genes, mobile genetic elements, and horizontal gene transfer events that contribute to its multidrug resistance. Notably, Breurec et al. (2016) conducted a comparative genomic analysis of Elizabethkingia anophelis isolates from different geographical regions, demonstrating significant genomic plasticity and the presence of resistance determinants associated with β-lactams, fluoroquinolones, and aminoglycosides [68]. Similarly, Wang et al. (2019) identified mutations in topoisomerase genes (gyrA and parC), which correlate with fluoroquinolone resistance, further emphasizing the role of genetic adaptations in antibiotic resistance [69].
Teo et al. identified E. anopheles in the midgut of Anopheles mosquitoes, suggesting that these insects may act as vectors for environmental transmission, raising alarms about possible reservoirs in natural habitats [70]. Hem et al. further corroborated this by demonstrating that Elizabethkingia species are prevalent in aquatic environments, which could facilitate their spread in hospital settings [4].
The presence of plasmid-mediated resistance genes and integrative conjugative elements in Elizabethkingia species indicate a capacity for horizontal gene transfer, complicating infection control efforts [71]. Larkin et al. emphasized the importance of distinguishing between E. anophelis and other species due to their differing resistance profiles, which can influence treatment outcomes [72]. Continued genomic studies are essential for understanding the epidemiology and transmission dynamics of Elizabethkingia spp., particularly in healthcare settings.
In addition to large-scale studies, smaller-scale case reports have contributed valuable insights into the clinical implications of Elizabethkingia infections. For instance, Johny et al. reported a case of septicemia and meningitis in a six-month-old due to E. meningosepticum, highlighting discrepancies between disc diffusion and MIC results in antimicrobial susceptibility testing [73]. Bellais et al. (2000) discovered a carbapenem-hydrolyzing beta-lactamase gene in E. meningosepticum, further indicating a clinical threat [74]. Studies on nosocomial pneumonia in infants post-cardiac surgery [75] and catheter-related bacteremia in patients with chronic renal failure [76] underscore the clinical relevance of E. meningosepticum. Despite smaller sample sizes, these case reports offer crucial insights into the pathogen’s resistance profiles and highlight the urgent need for new therapeutic strategies. The consistent resistance patterns across different regions and settings point to entrenched resistance mechanisms that may be evolving, emphasizing the necessity of developing new antibiotics or alternative therapies [77].
While this meta-analysis offers significant insights into the global antibiotic resistance patterns of Elizabethkingia, several limitations must be acknowledged. First, the reliance on observational data introduces inherent biases, including potential publication bias and variability in study design, sample size, and laboratory methodologies. Despite our efforts to standardize resistance data using internationally recognized breakpoints (CLSI and EUCAST), differences in susceptibility testing protocols across studies could contribute to measurement inconsistencies. Furthermore, excluding non-English studies and case reports may limit the generalizability of our findings, particularly in regions where these types of studies are more common. Although subgroup and temporal analyses were conducted, the lack of statistical significance in some comparisons, such as by geographic region and testing method, may reflect insufficient sample sizes or underpowered subgroups. Finally, while the fill-and-trim method was employed to adjust for potential publication bias, the possibility of unpublished or underreported studies influencing the overall conclusions cannot be entirely ruled out. These limitations should be considered when interpreting the results, and further large-scale, standardized studies are needed to validate and refine these findings.
5. Conclusion
This systematic review and meta-analysis provide a comprehensive assessment of Elizabethkingia antibiotic resistance patterns, revealing high resistance rates to key antibiotics, including cefotaxime (96.1%), meropenem (92.4%), and ceftazidime (88.5%). These findings indicate widespread and entrenched resistance mechanisms across regions and study periods. While resistance to rifampin (12.5%) and ciprofloxacin (27.9%) was lower, significant heterogeneity suggests regional variations in resistance patterns. The substantial differences in sulfamethoxazole resistance, ranging from 3.7% in Taiwan to 89.2% in India, further emphasize the need for localized treatment strategies. Additionally, the lack of a clear increasing or decreasing trend in resistance over time, as identified in the meta-regression analysis, suggests that resistance levels remain persistently high. These results highlight the urgent need for new therapeutic options, standardized susceptibility testing, and continuous surveillance to mitigate the spread of Elizabethkingia resistance. By aligning the conclusion with the study’s findings, this research underscores the necessity of global efforts to address this emerging nosocomial pathogen.
Supporting information
S1 File. S1 Table: Comprehensive Data and Characteristics Extracted from Included Articles.
S2 Table: JBI Checklist: Quality Assessment Results for Each Included Study. S3 Table: Meta-analysis statistics of worldwide antibiotic resistance in Elizabethkingia.
https://doi.org/10.1371/journal.pone.0323313.s001
(DOCX)
S1 Fig. Meta-regression analysis of antibiotic resistance trends over time for selected antibiotics.
Plots A through D represent the proportion of resistance found in studies for Rifampin, Ciprofloxacin, Gentamicin, and Sulfamethoxazole, respectively, from 1998 to 2023. Each circle denotes individual research, with the size indicating its relative weight. Solid lines illustrate the 95% confidence interval prediction of the resistance proportion, whereas dashed lines show the confidence intervals for each study. The analyses revealed no statistically significant trends in resistance to these antibiotics over time, as indicated by the p-values and confidence intervals.
https://doi.org/10.1371/journal.pone.0323313.s003
(JPEG)
Acknowledgments
The authors extend their appreciation to the University Higher Education at King Khalid University.
References
- 1. Ferreira CM, Barbosa MDNS, Ferreira GMA, Cristino JS, Alves CDS, Vieira EDS, et al. Multidrug-resistant Elizabethkingia meningoseptica and Enterococcus faecium infection in an oncohematologic patient. Am J Case Rep. 2024;25:e945360. pmid:39573829
- 2. Singh N, Singh A, Gupta P, Agarwal A. Elizabethkingia anophelis infections: a case series from a tertiary care hospital in Uttar Pradesh. Cureus. 2022.
- 3. Lau SKP, Chow W-N, Foo C-H, Curreem SOT, Lo GC-S, Teng JLL, et al. Elizabethkingia anophelis bacteremia is associated with clinically significant infections and high mortality. Sci Rep. 2016;6:26045. pmid:27185741
- 4. Hem S, Jarocki VM, Baker DJ, Charles IG, Drigo B, Aucote S, et al. Genomic analysis of Elizabethkingia species from aquatic environments: evidence for potential clinical transmission. Curr Res Microb Sci. 2021;3:100083. pmid:34988536
- 5. Wu C, Xiong L, Liao Q, Zhang W, Xiao Y, Xie Y. Clinical manifestations, antimicrobial resistance and genomic feature analysis of multidrug-resistant Elizabethkingia strains. Ann Clin Microbiol Antimicrob. 2024;23(1):32. pmid:38600542
- 6.
Johnson WL. Illuminating the black box: aspects of Elizabethkingia epidemiology and antimicrobial resistance. Oklahoma State University; 2020.
- 7. Lin J-N, Lai C-H, Yang C-H, Huang Y-H. Elizabethkingia infections in humans: from genomics to clinics. Microorganisms. 2019;7(9):295. pmid:31466280
- 8. Chen S, Pham S, Terrapon N, Blom J, Walker ED. Elizabethkingia anophelis MSU001 Isolated from Anopheles stephensi: molecular characterization and comparative genome analysis. Microorganisms. 2024;12(6):1079. pmid:38930461
- 9. Han M-S, Kim H, Lee Y, Kim M, Ku NS, Choi JY, et al. Relative prevalence and antimicrobial susceptibility of clinical isolates of Elizabethkingia species based on 16S rRNA gene sequencing. J Clin Microbiol. 2016;55(1):274–80. pmid:27847376
- 10. Burnard D, Henderson L, Ranasinghe A, Bergh H, Cottrell K, Sarovich D, et al. Comparative genomics and antimicrobial resistance profiling of Elizabethkingia isolates reveal nosocomial transmission and in vitro susceptibility to fluoroquinolones, tetracyclines, and trimethoprim-sulfamethoxazole. J Clin Microbiol. 2020;58(9).
- 11. Gao H, Li T, Li F, Zhang S. Elizabethkingia miricola causes intracranial infection: a case study. Front Med. 2021.
- 12. Bulagonda EP, Manivannan B, Mahalingam N, Lama M, Chanakya PP, Khamari B, et al. Comparative genomic analysis of a naturally competent Elizabethkingia anophelis isolated from an eye infection. Sci Rep. 2018;8(1):8447. pmid:29855598
- 13. Jian M-J, Cheng Y-H, Perng C-L, Shang H-S. Molecular typing and profiling of topoisomerase mutations causing resistance to ciprofloxacin and levofloxacin in Elizabethkingia species. PeerJ. 2018;6:e5608. pmid:30225179
- 14. Wang L, Zhang X, Li D, Hu F, Wang M, Guo Q, et al. Molecular characteristics and antimicrobial susceptibility profiles of Elizabethkingia clinical isolates in Shanghai, China. Infect Drug Resist. 2020;13:247–56. pmid:32099417
- 15. Chang Y, Zhang D, Niu S, Chen Q, Lin Q, Zhang X. MBLs, Rather Than Efflux Pumps, Led to Carbapenem Resistance in Fosfomycin and Aztreonam/Avibactam Resistant Elizabethkingia anophelis. Infect Drug Resist. 2021;14:315–27. pmid:33551643
- 16.
Leal J, Conly J, Henderson E, Manns B. How externalities impact an evaluation of strategies to prevent antimicrobial resistance in health care organizations. Antimicrobial Resistance and Infection Control; 2017.
- 17.
Afzal M. Antibiotic stewardship: battle to defeat superbugs. Annals of King Edward Medical University; 2017.
- 18. Lin I-F, Lai C-H, Lin S-Y, Lee C-C, Lee N-Y, Liu P-Y, et al. Mutant prevention concentrations of ciprofloxacin and levofloxacin and target gene mutations of fluoroquinolones in Elizabethkingia anophelis. Antimicrob Agents Chemother. 2022;66(7):e0030122. pmid:35708332
- 19. Kelly A, Karpathy S, Gulvik C, Ivey M, Whitney A, Bell M. A real-time multiplex pcr assay for detection of Elizabethkingia species and differentiation between Elizabethkingia anophelis and e. meningoseptica. J Clin Microbiol. 2019.
- 20. Choi M, Kim M-S, Jeong S, Choi J, Lee I, Yong T. Risk factors for Elizabethkingia acquisition and clinical characteristics of patients, South Korea. Emerg Infect Dis. 2019.
- 21. Seong H, Kim J, Lee W, Ahn J, Ku N, Choi J. Risk factors for mortality in patients with Elizabethkingia infection and the clinical impact of the antimicrobial susceptibility patterns of Elizabethkingia species. J Clin Med. 2020.
- 22. Page M, McKenzie J, Bossuyt P, Boutron I, Hoffmann T, Mulrow C. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372.
- 23. Migliavaca CB, Stein C, Colpani V, Munn Z, Falavigna M, Prevalence Estimates Reviews – Systematic Review Methodology Group (PERSyst). Quality assessment of prevalence studies: a systematic review. J Clin Epidemiol. 2020;127:59–68. pmid:32679313
- 24. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. pmid:3802833
- 25. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10(1):101.
- 26. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58. pmid:12111919
- 27. Viechtbauer W, Cheung MW-L. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112–25. pmid:26061377
- 28. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101. pmid:7786990
- 29.
Sterne JA, Egger M. Regression methods to detect publication and other bias in meta‐analysis. Publication Bias in Meta‐Analysis: Prevention, Assessment and Adjustments; 2005. p. 99–110.
- 30. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48.
- 31. Kuhn M, Wing J, Weston S, Williams A, Keefer C, Engelhardt A. Package ‘caret’. R J. 2020;223(7).
- 32. Adeyemi AI, Sulaiman AA, Solomon BB, Chinedu OA, Victor IA. Bacterial bloodstream infections in HIV-infected adults attending a Lagos teaching hospital. J Health Popul Nutr. 2010;28(4):318–26. pmid:20824974
- 33. Arega B, Wolde-Amanuel Y, Adane K, Belay E, Abubeker A, Asrat D. Rare bacterial isolates causing bloodstream infections in Ethiopian patients with cancer. Infect Agent Cancer. 2017;12:40. pmid:28702079
- 34. Barnawi AI, Kordy FN, Almuwallad OK, Kassarah KA. Early neonatal sepsis and meningitis caused by Elizabethkingia meningoseptica in Saudi Arabia. Saudi Med J. 2020;41(7):753–6. pmid:32601645
- 35. Bayrak B, Fıncanci M, Bınay UD, Çımen C, Özkantar Ünlügüneş GU. Elizabethkingia meningosepticum bacteremia in a patient with Bardet-Biedl syndrome and chronic renal failure. Mikrobiyol Bul. 2014;48(3):495–500. pmid:25052117
- 36. Bellais S, Aubert D, Naas T, Nordmann P. Molecular and biochemical heterogeneity of class B carbapenem-hydrolyzing beta-lactamases in Chryseobacterium meningosepticum. Antimicrob Agents Chemother. 2000;44(7):1878–86. pmid:10858348
- 37. Burnard D, Gore L, Henderson A, Ranasinghe A, Bergh H, Cottrell K. Comparative genomics and antimicrobial resistance profiling of Elizabethkingia isolates reveal nosocomial transmission and in vitro susceptibility to fluoroquinolones, tetracyclines, and trimethoprim-sulfamethoxazole. J Clin Microbiol. 2020;58(9).
- 38. Chang T-Y, Chen H-Y, Chou Y-C, Cheng Y-H, Sun J-R. In vitro activities of imipenem, vancomycin, and rifampicin against clinical Elizabethkingia species producing BlaB and GOB metallo-beta-lactamases. Eur J Clin Microbiol Infect Dis. 2019;38(11):2045–52. pmid:31352669
- 39. Costa AR da, Chideroli RT, Chicoski LM, Abreu DC de, Favero LM, Ferrari NA, et al. Frequency of pathogens in routine bacteriological diagnosis in fish and their antimicrobial resistance. Semina: Ciênc Agrár. 2021;42(6):3259–72.
- 40. Di Pentima MC, Mason EO Jr, Kaplan SL. In vitro antibiotic synergy against Flavobacterium meningosepticum: implications for therapeutic options. Clin Infect Dis. 1998;26(5):1169–76. pmid:9597247
- 41. Emele FE. Etiologic spectrum and pattern of antimicrobial drug susceptibility in bacterial meningitis in Sokoto, Nigeria. Acta Paediatr. 2000;89(8):942–6. pmid:10976835
- 42. Gungor S, Ozen M, Akinci A, Durmaz R. A Chryseobacterium meningosepticum outbreak in a neonatal ward. Infect Control Hosp Epidemiol. 2003;24(8):613–7.
- 43. Hashmi AW, Ahmad M, Israr MM, Fajar IE, Adnan F. Multi-drug-resistant Elizabethkingia meningoseptica: a rare cause of late-onset sepsis in a preterm neonate. Cureus. 2023;15(1):e34361. pmid:36874723
- 44. Hu R, Zhang Q, Gu Z. Whole-genome analysis of the potentially zoonotic Elizabethkingia miricola FL160902 with two new chromosomal MBL gene variants. J Antimicrob Chemother. 2020;75(3):526–30. pmid:31840164
- 45. Jiang X, Wang D, Wang Y, Yan H, Shi L, Zhou L. Occurrence of antimicrobial resistance genes sul and dfrA12 in hospital environmental isolates of Elizabethkingia meningoseptica. World J Microbiol Biotechnol. 2012;28(11):3097–102. pmid:22806748
- 46. Johnson WL, Ramachandran A, Torres NJ, Nicholson AC, Whitney AM, Bell M, et al. The draft genomes of Elizabethkingia anophelis of equine origin are genetically similar to three isolates from human clinical specimens. PLoS ONE. 2018;13(7):e0200731.
- 47. Johnston GCA, Lumsden JM. Antimicrobial susceptibility of bacterial isolates from 33 thoroughbred horses with arytenoid chondropathy (2005-2019). Vet Surg. 2020;49(7):1283–91. pmid:32767594
- 48. Johny M, Khuffash F, Elhag K. Antimicrobial treatment of flavobacterium meningosepticum infection. Ann Trop Paediatr. 1983;3(3):125–8.
- 49. Joshi P, Shah B, Joshi V, Kumar A, Singhal T. Treatment of Elizabethkingia meningoseptica neonatal meningitis with combination systemic and intraventricular therapy. Indian J Pediatr. 2019;86(4):379–81. pmid:30790188
- 50. Kämpfer P, Matthews H, Glaeser SP, Martin K, Lodders N, Faye I. Elizabethkingia anophelis sp. nov., isolated from the midgut of the mosquito Anopheles gambiae. Int J Syst Evol Microbiol. 2011;61(Pt 11):2670–5. pmid:21169462
- 51. Kirby JT, Sader HS, Walsh TR, Jones RN. Antimicrobial susceptibility and epidemiology of a worldwide collection of Chryseobacterium spp: report from the SENTRY antimicrobial surveillance program (1997-2001). J Clin Microbiol. 2004;42(1):445–8.
- 52. Kuo S-C, Tan M-C, Huang W-C, Wu H-C, Chen F-J, Liao Y-C, et al. Susceptibility of Elizabethkingia spp. to commonly tested and novel antibiotics and concordance between broth microdilution and automated testing methods. J Antimicrob Chemother. 2021;76(3):653–8. pmid:33258923
- 53. Li S, Wang X, Lu Y, Wang J, Yu D, Zhou Z, et al. Co-infections of Klebsiella pneumoniae and Elizabethkingia miricola in black-spotted frogs (Pelophylax nigromaculatus). Microb Pathog. 2023;180:106150. pmid:37196678
- 54. Naidenov B, Lim A, Willyerd K, Torres NJ, Johnson WL, Hwang HJ, et al. Pan-genomic and polymorphic driven prediction of antibiotic resistance in Elizabethkingia. Front Microbiol. 2019;10:1446. pmid:31333599
- 55. Ozkalay N, Anil M, Agus N, Helvaci M, Sirti S. Community-acquired meningitis and sepsis caused by Chryseobacterium meningosepticum in a patient diagnosed with thalassemia major. J Clin Microbiol. 2006;44(8):3037–9.
- 56. Sarathi S, Behera B, Mahapatra A, Mohapatra S, Jena J, Nayak S. Microbiological characterization and clinical facets of Elizabethkingia bloodstream infections in a tertiary care hospital of Eastern India. Infect Drug Resist. 2023;16:3257–67. pmid:37249959
- 57. Sedigh Ebrahim-Saraie H, Heidari H, Khashei R, Nabavizadeh SH. A rare case of complicated pericardial effusion with Elizabethkingia meningoseptica from Iran. Cell Mol Biol (Noisy-le-grand). 2018;64(3):53–5. pmid:29506640
- 58. Seong H, Kim J, Kim J, Lee W, Ahn J, M D. Risk factors for mortality in patients with Elizabethkingia infection and the clinical impact of the antimicrobial susceptibility patterns of Elizabethkingia species. J Clin Med. 2020;9(5).
- 59. Shailaja VV, Reddy AK, Alimelu M, Sadanand LNR. Neonatal meningitis by multidrug resistant Elizabethkingia meningosepticum identified by 16S ribosomal RNA gene sequencing. Int J Pediatr. 2014;2014:918907. pmid:24678326
- 60. Tak V, Mathur P, Varghese P, Misra MC. Elizabethkingia meningoseptica: an emerging pathogen causing meningitis in a hospitalized adult trauma patient. Indian J Med Microbiol. 2013;31(3):293–5. pmid:23883720
- 61. Tan L, Sun X, Zhu X, Zhang Z, Li J, Shu Q. Epidemiology of nosocomial pneumonia in infants after cardiac surgery. Chest. 2004;125(2):410–7. pmid:14769717
- 62. Wang L, Chen H, Liu W, Yang L, Xu Z, Chen D. Resistome and genome analysis of an extensively drug-resistant Klebsiella michiganensis KMIB106: characterization of a novel KPC plasmid pB106-1 and a novel cointegrate plasmid pB106-IMP harboring blaIMP-4 and blaSHV-12. Antibiotics (Basel). 2023;12(9):1463. pmid:37760759
- 63. Wei D, Cheng Y, Xiao S, Liao W, Yu Q, Han S, et al. Natural occurrences and characterization of Elizabethkingia miricola infection in cultured bullfrogs (Rana catesbeiana). Front Cell Infect Microbiol. 2023;13:1094050. pmid:36998635
- 64. Xie Z, Zhou Y, Wang S, Mei B, Xu X, Wen W. First isolation and identification of Elizabethkingia meningoseptica from cultured tiger frog, Rana tigerina rugulosa. Vet Microbiol. 2009;138(1–2):140–4.
- 65. Xu L, Peng B, He Y, Cui Y, Hu Q, Wu Y. Isolation of Elizabethkingia anophelis from covid-19 swab kits. Front Microbiol. 2021;12:799150.
- 66. Sarathi S, Behera B, Mahapatra A, Mohapatra S, Jena J, Nayak S. Microbiological characterization and clinical facets of Elizabethkingia bloodstream infections in a tertiary care hospital of Eastern India. Infect Drug Resist. 2023;16:3257–67. pmid:37249959
- 67. Jiang X, Wang D, Wang Y, Yan H, Shi L, Zhou L. Occurrence of antimicrobial resistance genes sul and dfrA12 in hospital environmental isolates of Elizabethkingia meningoseptica. World J Microbiol Biotechnol. 2012;28(11):3097–102. pmid:22806748
- 68. Breurec S, Criscuolo A, Diancourt L, Rendueles O, Vandenbogaert M, Passet V, et al. Genomic epidemiology and global diversity of the emerging bacterial pathogen Elizabethkingia anophelis. Sci Rep. 2016;6:30379. pmid:27461509
- 69. Wang M, Gao H, Lin N, Zhang Y, Huang N, Walker ED, et al. The antibiotic resistance and pathogenicity of a multidrug-resistant Elizabethkingia anophelis isolate. Microbiologyopen. 2019;8(11):e804. pmid:30891912
- 70. Teo J, Tan SY-Y, Liu Y, Tay M, Ding Y, Li Y, et al. Comparative genomic analysis of malaria mosquito vector-associated novel pathogen Elizabethkingia anophelis. Genome Biol Evol. 2014;6(5):1158–65. pmid:24803570
- 71. Fu J, Zhong C, Zhou Y, Lu M, Zong G, Zhang P, et al. The integrative and conjugative element ICECspPOL2 contributes to the outbreak of multi-antibiotic-resistant bacteria for Chryseobacterium Spp. and Elizabethkingia Spp. Microbiol Spectr. 2021;9(3):e0200521. pmid:34937181
- 72. Larkin PMK, Mortimer L, Malenfant JH, Gaynor P, Contreras DA, Garner OB, et al. Investigation of phylogeny and drug resistance mechanisms of Elizabethkingia anophelis isolated from blood and lower respiratory tract. Microb Drug Resist. 2021;27(9):1259–64. pmid:33656389
- 73. Johny M, Khuffash FA, Elhag KM. Antimicrobial treatment of Flavobacterium meningosepticum infection. Ann Trop Paediatr. 1983;3(3):125–8. pmid:6197021
- 74. Bellais S, Aubert D, Naas T, Nordmann P. Molecular and biochemical heterogeneity of class B carbapenem-hydrolyzing beta-lactamases in Chryseobacterium meningosepticum. Antimicrob Agents Chemother. 2000;44(7):1878–86. pmid:10858348
- 75. Tan L, Sun X, Zhu X, Zhang Z, Li J, Shu Q. Epidemiology of nosocomial pneumonia in infants after cardiac surgery. Chest. 2004;125(2):410–7. pmid:14769717
- 76. Bayrak B, Fıncanci M, Bınay UD, Çımen C, Özkantar Ünlügüneş GU. Elizabethkingia meningosepticum bacteremia in a patient with Bardet-Biedl syndrome and chronic renal failure. Mikrobiyol Bul. 2014;48(3):495–500. pmid:25052117
- 77. Arega B, Wolde-Amanuel Y, Adane K, Belay E, Abubeker A, Asrat D. Rare bacterial isolates causing bloodstream infections in Ethiopian patients with cancer. Infect Agent Cancer. 2017;12:40. pmid:28702079