Evidence map›Paper›PMID 39617907›Full record

ArticleGenome medicine2024

Comprehensive pathogen identification and antimicrobial resistance prediction from positive blood cultures using nanopore sequencing technology.

Po-Yu Liu, Han-Chieh Wu, Ying-Lan Li, Hung-Wei Cheng, Ci-Hong Liou, Feng-Jui Chen, Yu-Chieh Liao

Abstract read
In one paragraph

Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  6. Antibiotic resistance inFrontiers in microbiology · 2026
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  10. Advances in the detection of Drug-Resistant bacteria: current trends and innovations.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026
    Review
  11. Article
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  14. GGut microbes · 2025
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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.

Po-Yu LiuDivision of Infectious Diseases, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
Han-Chieh WuNational Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli County, Zhunan, Taiwan.
Ying-Lan LiDivision of Infectious Diseases, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
Hung-Wei ChengInstitute of Population of Health Sciences, National Health Research Institutes, Miaoli County, Zhunan, Taiwan.
Ci-Hong LiouNational Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli County, Zhunan, Taiwan.
Feng-Jui ChenNational Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli County, Zhunan, Taiwan. frchen@nhri.edu.tw.
Yu-Chieh LiaoInstitute of Population of Health Sciences, National Health Research Institutes, Miaoli County, Zhunan, Taiwan. jade@nhri.edu.tw.ORCID 0000-0002-4360-7932

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBlood cultures are essential for diagnosing bloodstream infections, but current phenotypic tests for antimicrobial resistance (AMR) provide limited information. Oxford Nanopore Technologies introduces nanopore sequencing with adaptive sampling, capable of real-time host genome depletion, yet its application directly from blood cultures remains unexplored. This study aimed to identify pathogens and predict AMR using nanopore sequencing.

methodsIn this cross-sectional genomic study, 458 positive blood cultures from bloodstream infection patients in central Taiwan were analyzed. Parallel experiments involved routine microbiologic tests and nanopore sequencing with a 15-h run. A bioinformatic pipeline was proposed to analyze the real-time sequencing reads. Subsequently, a comparative analysis was performed to evaluate the performance of species identification and AMR prediction.

resultsThe pipeline identified 76 species, with 88 Escherichia coli, 74 Klebsiella pneumoniae, 43 Staphylococcus aureus, and 9 Candida samples. Novel species were also discovered. Notably, precise species identification was achieved not only for monomicrobial infections but also for polymicrobial infections, which was detected in 23 samples and further confirmed by full-length 16S rRNA amplicon sequencing. Using a modified ResFinder database, AMR predictions showed a categorical agreement rate exceeding 90% (3799/4195) for monomicrobial infections, with minimal very major errors observed for K. pneumoniae (2/186, 1.1%) and S. aureus (1/90, 1.1%).

conclusionsNanopore sequencing with adaptive sampling can directly analyze positive blood cultures, facilitating pathogen detection, AMR prediction, and outbreak investigation. Integrating nanopore sequencing into clinical practices signifies a revolutionary advancement in managing bloodstream infections, offering an effective antimicrobial stewardship strategy, and improving patient outcomes.

Indexed as

Nanopore SequencingAnti-Bacterial AgentsBacteremiaBacteriaBlood CultureCross-Sectional StudiesDrug Resistance, BacterialHumansKlebsiella pneumoniaeRNA, Ribosomal, 16SAnti-Bacterial AgentsRNA, Ribosomal, 16SAdaptive samplingAntimicrobial resistance predictionNanopore sequencingPathogen identificationPositive blood culturesReal-time

Identifiers

PMID39617907
PMCPMC11610257

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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.