オックスフォード大学研究者が血流感染症診断を高速化する手法を開発(Oxford scientists devise method to speed up diagnosis of bloodstream infections)

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2026-05-19 オックスフォード大学

英オックスフォード大学の研究チームは、血流感染症(BSI)の診断を大幅に迅速化する新手法を開発した。血流感染症は敗血症など重篤化リスクが高く、早期診断と適切な抗菌薬投与が生存率を左右するが、従来の培養検査には数日を要する課題があった。新技術では、血液サンプル中の病原体を高感度に解析し、従来より短時間で感染原因を特定できる。研究チームは、分子生物学的解析と先進的データ処理技術を組み合わせることで、細菌種の迅速識別と抗菌薬耐性情報の取得を可能にした。これにより、患者ごとに最適な治療開始を早め、不要な広域抗菌薬使用の抑制にもつながる可能性がある。研究者らは、本技術が病院内感染対策や抗菌薬耐性問題への対応にも貢献すると期待しており、将来的には臨床現場への迅速導入を目指している。

オックスフォード大学研究者が血流感染症診断を高速化する手法を開発(Oxford scientists devise method to speed up diagnosis of bloodstream infections)
The Oxford Nanopore sequencing device used in the study. Image credit: Oxford Nanopore.

<関連情報>

オックスフォードナノポアシーケンスを用いた陽性血液培養からの一般的な未検出および培養不能な血流感染症の迅速診断:メタゲノムパイプライン解析 Rapid diagnosis of common, undetected, and uncultivable bloodstream infections from positive blood cultures using Oxford Nanopore sequencing: a metagenomic pipeline analysis

Dr Kumeren N Govender MBChB DPhil, Teresa L Street PhD, Nicholas D Sanderson PhD, Laura Leach MSc, Marcus Morgan MSc, Prof David W Eyre BM BCh DPhil
The Lancet Microbe  Available online: 14 May 2026
DOI:https://doi.org/10.1016/j.lanmic.2025.101333

Summary

Background

Metagenomic sequencing can potentially transform clinical microbiology by enabling rapid pathogen identification and antimicrobial resistance (AMR) prediction in critically ill patients with bloodstream infections. However, the clinical use of metagenomic sequencing has been constrained by its speed, accuracy, and technical feasibility. Our aim was to develop and evaluate a direct-from-positive blood culture workflow using Oxford Nanopore sequencing that overcomes these limitations and delivers rapid, accurate results.

Methods

In this metagenomic pipeline analysis, 211 positive (130 aerobic and 81 anaerobic) and 62 negative (30 aerobic and 32 anaerobic) randomly selected blood cultures were processed from Oxford University Hospitals for comparing species identification, AMR detection, and time-to-result against standard culture-based diagnostics performed by the hospital’s routine microbiology laboratory. Species prediction was performed using Kraken2 with a comprehensive standard database, applying heuristic and random forest classification models. Additionally, we benchmarked AMR classification tools and databases, including ResFinder, CARD, and NCBI AMRFinderPlus.

Findings

Across all samples, our method achieved 97% sensitivity and 94% specificity for species identification compared with that of routine culture and matrix-assisted laser desorption ionisation time-of-flight-based diagnostics; both sensitivity and specificity increased to 100% after adjudication of plausible additional infections. We detected 19 additional infections (13 polymicrobial, five previously unidentifiable, and one in a culture-negative sample) and delivered species identification results within 3 h 20 min (IQR 3 h 7 min–3 h 27 min), approximately 10 h earlier than routine diagnostic methods. For the ten most common clinically relevant pathogens, our method yielded AMR results 20 h earlier than current antimicrobial susceptibility testing, with an overall sensitivity of 88% and specificity of 93%. Performance varied by species. For Staphylococcus aureus, the AMR prediction sensitivity was 100% and specificity was 99%, and for Escherichia coli, the prediction sensitivity was 91% and specificity was 94%.

Interpretation

These findings show that metagenomic sequencing has the potential to rapidly and comprehensively detect pathogens and AMR in bloodstream infections. Integration into clinical practice could help to close diagnostic gaps, reduce empirical antibiotic use, and enable rapid targeted treatment. Nonetheless, improvements in AMR prediction for some species and drugs, along with further multisite validation, are required before clinical implementation.

Funding

National Institute for Health Research (NIHR) Oxford Biomedical Research Centre.

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