Trends in antimicrobial resistance in bloodstream infections at a large tertiary-care hospital in China: a 10-year retrospective study (2010–2019)

Liu, Cailin; Xu, Ming; Li, Xiaogai; Dong, Huiyue; Ming, Liang

doi:10.1016/j.jgar.2021.09.018

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…A study on trends in antimicrobial resistance in BSI at the first affiliated hospital of Zhengzhou University in China indicated E. coli, K. pneumoniae, S. aureus, A. baumannii and P. aeruginosa were the most common pathogen. 15 Our study showed that S. typhi was also a main BSI-associated pathogen in Hubei Province during 1998-2003. Typhoid fever is a poverty-related disease, mainly occurring in Africa and Asia, with a low incidence in economically developed regions such as Europe and the United States.…”

Section: Discussionmentioning

confidence: 62%

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Antimicrobial Resistance Among Pathogens Causing Bloodstream Infections: A Multicenter Surveillance Report Over 20 Years (1998–2017)

Zhang¹,

Sun²,

Tian³

2022

IDR

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Purpose Bloodstream infections (BSIs) are a common consequence of infectious diseases and cause high morbidity and mortality. Appropriate antibiotic use is critical for patients’ treatment and prognosis. Long-term monitoring and analysis of antimicrobial resistance are important in guiding physicians to choose appropriate antibiotics and understand the changes in antimicrobial resistance and infection control. Here, we report a retrospective study on the trends of antimicrobial resistance in the common BSI-associated pathogens. Methods The identification of strains and antimicrobial susceptibility tests were performed in each anticipating hospital independently. Data from the Hubei Province Antimicrobial Resistance Surveillance System (HBARSS) from 1998 to 2017 were retrospectively analyzed using WHONET 5.6 software. Results Data from HBARSS (1998–2017) revealed that 40,518 Gram-positive bacteria and 26,568 Gram-negative bacteria caused BSIs, the most common of which were Staphylococcus aureus and Escherichia coli. Salmonella typhi was a predominant BSI-associated pathogen in 1998–2003. Antimicrobial susceptibility data showed that the resistance rates of E. coli and Klebsiella pneumoniae to cefotaxime were significantly higher than those to ceftazidime. The proportion of strains of special antimicrobial resistance phenotypes including difficult-to-treat resistance (DTR), carbapenem-resistant (CR), extended-spectrum cephalosporin resistant (ECR) and fluoroquinolone resistant (FQR) in E. coli was 0.18%, 0.26%, 13.95%, 22.78% while in K. pneumoniae was 11.95%, 14.00%, 31.91% and 11.40%, respectively. In 2013–2017, K. pneumoniae showed resistance levels reaching 15.8% and 17.5% to imipenem and meropenem, respectively, and Acinetobacter baumannii showed high resistance rates ranging from 60 to 80% to common antibiotics. The detection rate of Salmonella typhi resistance to third-generation cephalosporins and fluoroquinolones was less than 5%. Control of methicillin-resistant Staphylococcus aureus (MRSA) remains a major challenge, and in 2009–2017, the MRSA detection rate was 40–50%. Conclusion Prevalence of CR K. pneumoniae has increased significantly in recent years. Resistance rates of A. baumannii to common antimicrobial agents have increased exponentially, reaching high levels. MRSA remains a challenge to control. For K. pneumoniae, DTR, CR, ECR and FQR were antimicrobial resistance phenotypes that could not be ignored while for E. coli DTR and CR were rare antimicrobial resistance phenotypes. CR K. pneumoniae, A. baumannii ...

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Antimicrobial Resistance Among Pathogens Causing Bloodstream Infections: A Multicenter Surveillance Report Over 20 Years (1998–2017)

Zhang¹,

Sun²,

Tian³

2022

IDR

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“…Globally, several studies and antimicrobial resistance surveillance systems have reported increasing rates of antimicrobial resistance among E. coli and K. pneumoniae. 8 , 9 , 10 , 11 , 12 , 13 For example, a 10-year retrospective study in China found that the rate of carbapenem-resistant K. pneumoniae increased significantly, from 6.7% in 2010 to 56.7% in 2019. 12 Similarly, a surveillance report from a hospital in Malawi revealed a substantial increase in ciprofloxacin-resistant E. coli (from 0.0% in 1998 to 31.1% in 2016) and ciprofloxacin-resistant Klebsiella species (from 1.7% in 1998 to 70.2% in 2016).…”

Section: Introductionmentioning

confidence: 99%

“… 8 , 9 , 10 , 11 , 12 , 13 For example, a 10-year retrospective study in China found that the rate of carbapenem-resistant K. pneumoniae increased significantly, from 6.7% in 2010 to 56.7% in 2019. 12 Similarly, a surveillance report from a hospital in Malawi revealed a substantial increase in ciprofloxacin-resistant E. coli (from 0.0% in 1998 to 31.1% in 2016) and ciprofloxacin-resistant Klebsiella species (from 1.7% in 1998 to 70.2% in 2016). 14 Several scientific reports out of Africa have emphasised the fast-growing threat of antimicrobial resistance.…”

Section: Introductionmentioning

confidence: 99%

Antimicrobial resistance trends in clinical Escherichia coli and Klebsiella pneumoniae in Ethiopia

Kitaba,

Bonger,

Beyene

et al. 2024

African Journal of Laboratory Medicine

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Background: Clinicians rely on local antimicrobial resistance pattern data to guide empiric treatment for seriously ill patients when culture and antimicrobial susceptibility testing results are not immediately available.Objective: This study aimed to analyse 5-year trends in antimicrobial resistance profiles of Escherichia coli and Klebsiella pneumoniae isolates.Methods: Bacteriology reports from 2017 to 2021 at the Ethiopian Public Health Institute were analysed retrospectively. Isolates were identified using either the VITEK 2 Compact system, the BD Phoenix M50 instrument, or conventional biochemical tests. Antimicrobial susceptibility testing was conducted using either the Kirby-Bauer disk diffusion method or the VITEK 2 Compact system and BD Phoenix M50 systems available at the time of testing. The Cochran Armitage trend test was employed to test the significance of antimicrobial resistance trends over time. P-values less than 0.05 were considered statistically significant.Results: Of the 5382 bacteriology reports examined, 458 (9%) were on E. coli and 266 (5%) were on K. pneumoniae. Both K. pneumoniae (88%) and E. coli (65%) demonstrated high resistance to extended-spectrum cephalosporins. However, both K. pneumoniae (14%) and E. coli (5%) showed lower rates of resistance to carbapenems compared to other antimicrobials. In K. pneumoniae, resistance to carbapenems (from 0% to 38%; p 0.001) and ciprofloxacin (from 41% to 90%; p 0.001) increased significantly between 2017 and 2021.Conclusion: Both organisms showed very high resistance to broad-spectrum antibiotics. Additionally, K. pneumoniae demonstrated a statistically significant rise in ciprofloxacin and carbapenem resistance.What this study adds: This study emphasises the significance of regular reporting of local antimicrobial resistance patterns as this information can guide appropriate empiric therapy and efforts to address antimicrobial resistance issues.

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“…Pathogenic P. aeruginosa alters genome sequences and protein expression to resist. Resistance disrupts biochemical pathways and protein channels [25]. Antibiotic resistance and susceptibility must be linked to specifc resistance genes; all genes in an isolate are added to predict susceptibility [26].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

Noman

Zeeshan

Arshad

et al. 2023

Computational Intelligence and Neuroscience

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Aim. Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa. Method. A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis. Results. Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains. Conclusion. The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.

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Trends in antimicrobial resistance in bloodstream infections at a large tertiary-care hospital in China: a 10-year retrospective study (2010–2019)

Cited by 9 publications

References 34 publications

Antimicrobial Resistance Among Pathogens Causing Bloodstream Infections: A Multicenter Surveillance Report Over 20 Years (1998–2017)

Antimicrobial Resistance Among Pathogens Causing Bloodstream Infections: A Multicenter Surveillance Report Over 20 Years (1998–2017)

Antimicrobial resistance trends in clinical Escherichia coli and Klebsiella pneumoniae in Ethiopia

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

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