Shedding light on negative cultures in osteoarticular infections: leveraging mNGS to unravel risk factors and microbial profiles - Ding, Haiqi; Huang, Jiexin; Lin, Lan; Chen, Yang; Wang, Qijin; Li, Wenbo; Huang, Ying; Fang, Xinyu; Zhang, Wenming (Frontiers in Cellular and Infection Microbiology, 2024)
BackgroundThe objective of this study is to utilize metagenomic next-generation sequencing (mNGS) to analyze the risk factors causing negative microbial cultures, comprehensively delineate the microbial profiles neglected by traditional cultures, and optimize the pathogenetic diagnostic procedure accordingly.Research design and methodsWe enrolled 341 patients diagnosed with OI at our center between 2016 and 2022, and gathered data including age, gender, clinical diagnosis, duration of antibiotic use prior to sampling, microbial culture results, and mNGS results for these patients. According to microbial detection results, risk factors for negative microbial culture and mNGS results were investigated through univariate and multivariate analyses, and the microbial profile in cases with negative microbial cultures was summarized in conjunction with mNGS results. Building upon this, we suggest strategies to enhance the positivity rate of microbial cultures based on clinical experience.ResultsInvasive osteoarticular infection (IOI), multi-infections, rare pathogen infections, and prior antibiotic use are risk factors for negative microbial cultures. When the duration of prior antibiotic use is ≥3 days, mNGS demonstrates significantly higher pathogen detection efficiency than microbial culture. Moreover, the risk of negative microbial culture increases by 4.8 times with the exposure to each additional risk factor (OR=4.043, 95%CI [2.835, 5.765], P<0.001). Additionally, over one-third of culture-negative OI involve polymicrobial infections or rare pathogens.ConclusionsClinicians should tailor microbial culture strategies based on patient conditions. When needed, they can collaborate with mNGS or optimize microbial culture conditions based on mNGS results to enhance the efficiency of pathogen diagnosis.