{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T23:06:59Z","timestamp":1792364819684,"version":"4.4.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Shanghai Antimicrobial Surveillance Network","award":["3030231003"],"award-info":[{"award-number":["3030231003"]}]},{"name":"China Antimicrobial Surveillance Network","award":["WI207259"],"award-info":[{"award-number":["WI207259"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Klebsiella pneumoniae is a significant pathogen with increasing resistance and high mortality rates. Conventional antibiotic susceptibility testing methods are time-consuming. Next-generation sequencing has shown promise for predicting antimicrobial resistance (AMR). This study aims to develop prediction models using whole-genome sequencing data and assess their feasibility with metagenomic next-generation sequencing data from clinical samples.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>On the basis of 4170 K. pneumoniae genomes, the main genetic characteristics associated with AMR were identified using a LASSO regression model. Consequently, the prediction model was established, validated and optimized using clinical isolate read simulation sequences. To evaluate the efficacy of the model, clinical specimens were collected.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Four predictive models for amikacin, ciprofloxacin, levofloxacin, and piperacillin\/tazobactam, initially had positive predictive values (PPVs) of 92%, 98%, 99%, 94%, respectively, when they were originally constructed. When applied to clinical specimens, their PPVs were 96%, 96%, 95%, and 100%, respectively. Meanwhile, there were negative predictive values (NPVs) of 100% for ciprofloxacin and levofloxacin, and \u2018not applicable\u2019 (NA) for amikacin and piperacillin\/tazobactam. Our method achieved antibacterial phenotype classification accuracy rates of 95.92% for amikacin, 96.15% for ciprofloxacin, 95.31% for levofloxacin and 100% for piperacillin\/tazobactam. The sequence-based prediction antibiotic susceptibility testing (AST) reported results in an average time of 19.5 h, compared with the 67.9 h needed for culture-based AST, resulting in a significant reduction of 48.4 h.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>These preliminary results demonstrated that the performance of prediction model for a clinically significant antimicrobial\u2013species pair was comparable to that of phenotypic methods, thereby encouraging the expansion of sequence-based susceptibility prediction and its clinical validation and application.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jac\/dkae248","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T16:53:09Z","timestamp":1721407989000},"page":"2509-2517","source":"Crossref","is-referenced-by-count":23,"title":["Prediction of antimicrobial resistance in <i>Klebsiella pneumoniae<\/i> using genomic and metagenomic next-generation sequencing data"],"prefix":"10.1093","volume":"79","author":[{"given":"Xun","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Antibiotics, Huashan Hospital, Fudan University , Shanghai ,","place":["China"]},{"name":"Key Laboratory of Clinical Pharmacology of Antibiotics, Ministry of Health , Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Air Force Military Medical University , Xi\u2019an ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Genskey Medical Technology Co. 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