Rapid nanopore sequencing and predictive susceptibility testing of positive blood cultures from intensive care patients with sepsis
- PMID: 38193658
- PMCID: PMC10846127
- DOI: 10.1128/spectrum.03065-23
Rapid nanopore sequencing and predictive susceptibility testing of positive blood cultures from intensive care patients with sepsis
Abstract
We aimed to evaluate the performance of Oxford Nanopore Technologies (ONT) sequencing from positive blood culture (BC) broths for bacterial identification and antimicrobial susceptibility prediction. Patients with suspected sepsis in four intensive care units were prospectively enrolled. Human-depleted DNA was extracted from positive BC broths and sequenced using ONT (MinION). Species abundance was estimated using Kraken2, and a cloud-based system (AREScloud) provided in silico predictive antimicrobial susceptibility testing (AST) from assembled contigs. Results were compared to conventional identification and phenotypic AST. Species-level agreement between conventional methods and AST predicted from sequencing was 94.2% (49/52), increasing to 100% in monomicrobial infections. In 262 high-quality AREScloud AST predictions across 24 samples, categorical agreement (CA) was 89.3%, with major error (ME) and very major error (VME) rates of 10.5% and 12.1%, respectively. Over 90% CA was achieved for some taxa (e.g., Staphylococcus aureus) but was suboptimal for Pseudomonas aeruginosa. In 470 AST predictions across 42 samples, with both high quality and exploratory-only predictions, overall CA, ME, and VME rates were 87.7%, 8.3%, and 28.4%. VME rates were inflated by false susceptibility calls in a small number of species/antibiotic combinations with few representative resistant isolates. Time to reporting from sequencing could be achieved within 8-16 h from BC positivity. Direct sequencing from positive BC broths is feasible and can provide accurate predictive AST for some species. ONT-based approaches may be faster but significant improvements in accuracy are required before it can be considered for clinical use.IMPORTANCESepsis and bloodstream infections carry a high risk of morbidity and mortality. Rapid identification and susceptibility prediction of causative pathogens, using Nanopore sequencing direct from blood cultures, may offer clinical benefit. We assessed this approach in comparison to conventional phenotypic methods and determined the accuracy of species identification and susceptibility prediction from genomic data. While this workflow holds promise, and performed well for some common bacterial species, improvements in sequencing accuracy and more robust predictive algorithms across a diverse range of organisms are required before this can be considered for clinical use. However, results could be achieved in timeframes that are faster than conventional phenotypic methods.
Keywords: bloodstream infection; long-read whole-genome sequencing; nanopore.
Conflict of interest statement
Lukas Lüftinger and Stephan Beisken are employees of Ares Genetics. Patrick Harris reports research grants from Gilead, has served on advisory boards for OpGen, Merck, and Sandoz, and has received honoraria from OpGen, Sandoz, Pfizer, and BioMerieux. David Paterson reports grants from Shionogi, Pfizer, Merck and bioMerieux, and consultancies with the AMR Action Fund, Entasis, QPex, Spero, VenatoRx, Pfizer, Merck, Gilead, bioMerieux, and Accelerate Diagnostics. Jason Roberts reports grants from Qpex, Gilead, Pfizer, Sandoz, MSD, Summit Pharma, and Cipla. Adam Irwin has received research grants and honoraria from Gilead, and honoraria from bioMerieux unrelated to this work.
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- Queensland Genomics/Queensland Government (State of Queensland)
- Health System Improvement Ideas Grant/Brisbane Diamantina Health Partners
- Early Career Research Fellowship (GNT1157530)/DHAC | National Health and Medical Research Council (NHMRC)
- Centre of Research Excellence (APP2007007)/DHAC | National Health and Medical Research Council (NHMRC)
- Investigator Grant (GNT1197743)/DHAC | National Health and Medical Research Council (NHMRC)
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