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Tracing the transmission of carbapenem-resistant Enterobacteriales at the patient:ward environmental nexus

Elton, L.; Williams, A.; Ali, S.; Heaphy, J.; Pang, V.; Commins, L.; O'Brien, C.; Yetis, O.; Caine, E.; Ward, I.; Muzslay, M.; Yui, S.; Karia, K.; Shore, E.; Rofael, S.; Mack, D.; McHugh, T. D.; Wey, E. Q.

2024-07-05 infectious diseases
10.1101/2024.07.03.24309291 medRxiv
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Structured summaryO_ST_ABSIntroductionC_ST_ABSColonisation and infection with Carbapenem-resistant Enterobacterales (CRE) in healthcare settings poses significant risks, especially for vulnerable patients. Genomic analysis can be used to trace transmission routes, supporting antimicrobial stewardship and informing infection control strategies. Here we used genomic analysis to track the movement and transmission of CREs within clinical and environmental samples. Methods25 isolates were cultured from clinical patient samples or swabs, that tested positive for OXA-48-like variants using the NG-Test(R) CARBA-5 test and whole genome sequenced (WGS) using Oxford Nanopore Technologies (ONT). 158 swabs and 52 wastewater samples were collected from the ward environment. 60 isolates (matching clinical isolate genera; Klebsiella, Enterobacter, Citrobacter and Escherichia) were isolated from the environmental samples. Metagenomic sequencing was undertaken on 36 environmental wastewater and swab samples. Results21/25 (84%) clinical isolates had >1 blaOXA gene and 19/25 (76%) harboured >1 blaNDM gene. Enterobacterales were most commonly isolated from environmental wastewater samples 27/52 (51.9%), then stick swabs 5/43 (11.6%) and sponge swabs 5/115 (4.3%). 11/60 (18%) environmental isolates harboured at >1 blaOXA gene and 1.9% (1/60) harboured blaNDM-1. blaOXA genes were found in 2/36 (5.5%) metagenomic environmental samples. DiscussionPotential for putative patient-patient and patient-ward transmission was shown. ONT sequencing can expedite clinical decisions whilst awaiting reference laboratory results, providing economic and patient care benefits. Metagenomic sampling needs optimization to improve sensitivity.

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