Prediction of antimicrobial resistance in Klebsiella pneumoniae using genomic and metagenomic next-generation sequencing data

作者：Xun Zhou, Ming Yang, Fangyuan Chen, Leilei Wang, Peng Han, Zhi Jiang, Siquan Shen, Guanhua Rao, Fan Yang · 发表于：Journal of Antimicrobial Chemotherapy · 年份：2024 · DOI：10.1093/jac/dkae248 · 被引用次数：17 · 研究领域：Antibiotic Resistance in Bacteria、Bacterial Identification and Susceptibility Testing、Genomics and Phylogenetic Studies

OBJECTIVES: 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. METHODS: 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. RESULTS: 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 'not applicable' (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...