Evidence map›Paper›PMID 39633294›Full record

SynthesisBMC infectious diseases2024

Mortality and genetic diversity of antibiotic-resistant bacteria associated with bloodstream infections: a systemic review and genomic analysis.

Haitao Zhao, Mengyue Liu, Jie Wu, Shuaiyin Chen, Haiyan Yang, Jinzhao Long, Guangcai Duan

Abstract readSystematic Review
In one paragraph

Synthesis in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Haitao ZhaoDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China.
Mengyue LiuDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China.
Jie WuDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China.
Shuaiyin ChenDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China.
Haiyan YangDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China.
Jinzhao LongDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China. ljz069@zzu.edu.cn.
Guangcai DuanDepartment of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, Henan, 450001, People's Republic of China. gcduan@zzu.edu.cn.

Funding

China Postdoctoral Science Foundation 2022M712859Henan Provincial Science and Technology Research Project 232102310018National Science and Technology Specific Projects 2018ZX10301407
6 · The paper itself

Abstract

backgroundBloodstream infections (BSIs) caused by antibiotic-resistant bacteria (ARB) represent a significant disease burden worldwide. However, a comprehensive analysis of the mortality rates and global epidemiology across different ARB species associated with BSIs is currently lacking.

methodsWe conducted a systematic review by searching multiple databases (PubMed, Web of Science, and Embase) for studies reporting ARB-related BSIs data up to June 19, 2023. Additionally, we performed genomic analyses of all the publicly available bacterial genomes associated with BSIs to elucidate their molecular characteristics.

resultsA total of 322 articles (N = 90,672 patients) were included in this study. For 28 or 30-day mortality, the overall mortality rate for all ARB species was 32.0%. Among them, antibiotic-resistant A. baumannii exhibited the highest rate (54.2%). And the top three ARB types with the highest mortality rates at 28 or 30-day were CefeR-PA (cefepime-resistant P. aeruginosa), CREC (carbapenem-resistant E. coli), and CRAB (carbapenem-resistant A. baumannii), all exceeding 50%, whereas the mortality rates of CRKP (carbapenem-resistant K. pneumoniae), CRPA (carbapenem-resistant P. aeruginosa), and VREfm (vancomycin-resistant E. faecium) were at least 40%. A total of 9,289 ARB genomes related to BSIs were acquired from the NPDIB database and are predominantly distributed in North America, Asia, and Europe. Antibiotic resistance gene (ARG) analysis identified a total of 613 ARG subtypes from the top six ARB species, with numbers ranging from 48 for E. faecium to 253 for K. pneumoniae. Furthermore, specific clones of ARB species were strongly associated with BSIs, such as ST131 in E. coli, ST8, and ST5 in S. aureus, ST2 in A. baumannii, and ST11 and ST258 in K. pneumoniae.

conclusionARB contributed to the burden of BSIs, with a 30-day all-cause mortality rate as high as 32.0%. ARB strains causing BSIs display high genetic diversity, highlighting the importance of continuing to monitor high-risk clones to control the development of antibiotic resistance. Differences in ARGs patterns require tailored antibiotic management strategies for each ARB species.

Indexed as

Anti-Bacterial AgentsBacteremiaGenetic VariationBacteriaDrug Resistance, BacterialGenome, BacterialGenomicsHumansAnti-Bacterial AgentsBacterial resistanceBloodstream infectionsGenomic epidemiologyMortality

Identifiers

PMID39633294
PMCPMC11619121

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.