Evaluation of a fully automated bioinformatics tool to predict antibiotic resistance from MRSA genomes. (5th February 2020)

• Record Type: Journal Article
• Authors: Kumar, Narender; Raven, Kathy E; Blane, Beth; Leek, Danielle; Brown, Nicholas M; Bragin, Eugene; Rhodes, Paul A; Parkhill, Julian; Peacock, Sharon J
• Is Part Of: Journal of antimicrobial chemotherapy. Volume 75:Number 5(2020)
• Page Start: 1117
• Page End: 1122
• Publication Date: 2020-02-05
• Subjects: Anti-infective agents -- Periodicals; Chemotherapy -- Periodicals; 615.58
• DOI: 10.1093/jac/dkz570
• Languages: English
• ISSNs: 0305-7453

Abstract

Abstract: Objectives: The genetic prediction of phenotypic antibiotic resistance based on analysis of WGS data is becoming increasingly feasible, but a major barrier to its introduction into routine use is the lack of fully automated interpretation tools. Here, we report the findings of a large evaluation of the Next Gen Diagnostics (NGD) automated bioinformatics analysis tool to predict the phenotypic resistance of MRSA. Methods: MRSA-positive patients were identified in a clinical microbiology laboratory in England between January and November 2018. One MRSA isolate per patient together with all blood culture isolates (total n = 778) were sequenced on the Illumina MiniSeq instrument in batches of 21 clinical MRSA isolates and three controls. Results: The NGD system activated post-sequencing and processed the sequences to determine susceptible/resistant predictions for 11 antibiotics, taking around 11 minutes to analyse 24 isolates sequenced on a single sequencing run. NGD results were compared with phenotypic susceptibility testing performed by the clinical laboratory using the disc diffusion method and EUCAST breakpoints. Following retesting of discrepant results, concordance between phenotypic results and NGD genetic predictions was 99.69%. Further investigation of 22 isolate genomes associated with persistent discrepancies revealed a range of reasons in 12 cases, but no cause could be found for the remainder. Genetic predictions generated by the NGD tool were compared with predictions generated by an independent research-based informatics approach, which demonstrated an overall concordance between the two methods of 99.97%. Conclusions: We conclude that the NGD system provides rapid and accurate prediction of the antibiotic susceptibility of MRSA.