Large-Scale Evaluation of a Rapid Fully Automated Analysis Platform to Detect and Refute Outbreaks Based on MRSA Genome Comparisons

Raven, Kathy E.; Bragin, Eugene; Blane, Beth; Leek, Danielle; Kumar, Narender; Rhodes, Paul; Enoch, David; Thaxter, Rachel; Brown, Nicholas; Parkhill, Julian; Peacock, Sharon J.

doi:10.1128/msphere.00283-22

Cited by 1 publication

(3 citation statements)

References 13 publications

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“…Genome analysis was performed using the automated NGD cloud-based bioinformatics system, as previously described [ 6 8 ]. In brief, on completion of the sequencing run this system uploaded raw fastq folders via an encrypted secure upload to the Amazon UK Web Services cloud, where 60 compute nodes were automatically activated.…”

Section: Methodsmentioning

confidence: 99%

“…A second study demonstrated that the tool provided highly accurate predictions of antibiotic susceptibility to 31 agents [ 7 ]. Evaluation was extended to 781 MRSA genomes from 777 consecutive patients, which was compared with analytic outputs from a bespoke researcher-led informatics pipeline [ 8 ]. There was full concordance between the two analysis methods for species identification, detection of mec genes and multi-locus sequence type (ST) assignment.…”

Section: Introductionmentioning

confidence: 99%

“…These had a median (range) SNP difference between the two methods of 1.2 SNPs (0–22 SNPs), with most isolate pairs (87 %) varying by ≤2 SNPs. Both pipelines clustered 338 isolates/334 patients into 66 unique clusters based on genetic relatedness, with the automated system identifying four putative clusters not detected by the researcher-led pipeline [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the impact of genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) on hospital infection prevention and control decisions

Blane,

Raven,

Brown

et al. 2024

Microbial Genomics

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Genomic epidemiology enhances the ability to detect and refute methicillin-resistant Staphylococcus aureus (MRSA) outbreaks in healthcare settings, but its routine introduction requires further evidence of benefits for patients and resource utilization. We performed a 12 month prospective study at Cambridge University Hospitals NHS Foundation Trust in the UK to capture its impact on hospital infection prevention and control (IPC) decisions. MRSA-positive samples were identified via the hospital microbiology laboratory between November 2018 and November 2019. We included samples from in-patients, clinic out-patients, people reviewed in the Emergency Department and healthcare workers screened by Occupational Health. We sequenced the first MRSA isolate from 823 consecutive individuals, defined their pairwise genetic relatedness, and sought epidemiological links in the hospital and community. Genomic analysis of 823 MRSA isolates identified 72 genetic clusters of two or more isolates containing 339/823 (41 %) of the cases. Epidemiological links were identified between two or more cases for 190 (23 %) individuals in 34/72 clusters. Weekly genomic epidemiology updates were shared with the IPC team, culminating in 49 face-to-face meetings and 21 written communications. Seventeen clusters were identified that were consistent with hospital MRSA transmission, discussion of which led to additional IPC actions in 14 of these. Two outbreaks were also identified where transmission had occurred in the community prior to hospital presentation; these were escalated to relevant IPC teams. We identified 38 instances where two or more in-patients shared a ward location on overlapping dates but carried unrelated MRSA isolates (pseudo-outbreaks); research data led to de-escalation of investigations in six of these. Our findings provide further support for the routine use of genomic epidemiology to enhance and target IPC resources.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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