{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T06:17:59Z","timestamp":1790662679175,"version":"4.1.0"},"reference-count":67,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Fonden","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010662","name":"H2020 Excellent Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010662","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    Antimicrobial susceptibility testing (AST) should be fast and accurate, leading to proper interventions and therapeutic success. Clinical microbiology laboratories rely on phenotypic methods, but the continuous improvement and decrease in the cost of whole-genome sequencing (WGS) technologies make them an attractive alternative. Studies evaluating the performance of WGS-based prediction of antimicrobial resistance (AMR) for selected bacterial species have shown promising results. There are, however, significant gaps in the literature evaluating the applicability of WGS as a diagnostics method in real-life clinical settings against the range of bacterial pathogens experienced there. Thus, we compared standard phenotypic AST results with WGS-based predictions of AMR profiles in bacterial isolates without preselection of defined species, to evaluate the applicability of WGS as a diagnostics method in clinical settings. We collected all bacterial isolates processed by all Danish Clinical Microbiology Laboratories in 1 day. We randomly selected 500 isolates without any preselection of species. We performed AST through standard broth microdilution (BMD) for 488 isolates (\n                    <jats:italic>n<\/jats:italic>\n                    = 6,487 phenotypic AST results) and compared results with\n                    <jats:italic>in silico<\/jats:italic>\n                    antibiograms obtained through WGS (Illumina NextSeq) followed by bioinformatics analyses using ResFinder 4.0 (\n                    <jats:italic>n<\/jats:italic>\n                    = 5,229 comparisons). A higher proportion of AMR was observed for Gram-negative bacteria (10.9%) than for Gram-positive bacteria (6.1%). Comparison of BMD with WGS data yielded a concordance of 91.7%, with discordant results mainly due to phenotypically susceptible isolates harboring genetic AMR determinants. These cases correspond to 6.2% of all isolate-antimicrobial combinations analyzed and to 6.8% of all phenotypically susceptible combinations. We detected fewer cases of phenotypically resistant isolates without any known genetic resistance mechanism, particularly 2.1% of all combinations analyzed, which corresponded to 26.4% of all detected phenotypic resistances. Most discordances were observed for specific combinations of species-antimicrobial: macrolides and tetracycline in streptococci, ciprofloxacin and \u03b2-lactams in combination with \u03b2-lactamase inhibitors in\n                    <jats:italic>Enterobacterales<\/jats:italic>\n                    , and most antimicrobials in\n                    <jats:italic>Pseudomonas aeruginosa<\/jats:italic>\n                    . WGS has the potential to be used for surveillance and routine clinical microbiology. However, in clinical microbiology settings and especially for certain species and antimicrobial agent combinations, further developments in AMR gene databases are needed to ensure higher concordance between\n                    <jats:italic>in silico<\/jats:italic>\n                    predictions and expected phenotypic AMR profiles.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2022.804627","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T02:02:28Z","timestamp":1654826548000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["One Day in Denmark: Comparison of Phenotypic and Genotypic Antimicrobial Susceptibility Testing in Bacterial Isolates From Clinical Settings"],"prefix":"10.3389","volume":"13","author":[{"given":"Ana Rita","family":"Rebelo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeria","family":"Bortolaia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pimlapas","family":"Leekitcharoenphon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis Schr\u00f8der","family":"Hansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hans Linde","family":"Nielsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svend","family":"Ellermann-Eriksen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Kemp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bent L\u00f8we","family":"R\u00f8der","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niels","family":"Frimodt-M\u00f8ller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Turid Snekloth","family":"S\u00f8ndergaard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John Eugenio","family":"Coia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claus","family":"\u00d8stergaard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henrik","family":"Westh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank M.","family":"Aarestrup","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1128\/mBio.00444-416","article-title":"Whole-Genome sequencing for routine pathogen surveillance in public health: a population snapshot of invasive Staphylococcus aureus in Europe.","volume":"7","author":"Aanensen","year":"2016","journal-title":"mBio"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1128\/JCM.01260-1220","article-title":"Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research.","volume":"59","author":"Anahtar","year":"2021","journal-title":"J. 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