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南美超级胎牛血清999尝鲜特惠
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基质胶阶梯特惠
积分商城,好礼送不停

宏基因组二代测序在ICU危重患者下呼吸道感染中的应用价值研究

The value of metagenomic next-generation sequencing in lower respiratory tract infections among critically ill patients in the ICU

Ying Xu, Weiwei Wu, Danjiang Dong, Ning Liu, Jia Liu, Baocui Qi, Qin Gu

Journal:Frontiers in Cellular and Infection Microbiology

IF:4.8

DOI:10.3389/fcimb.2026.1746117

PMID:41822332

Published:2026-02-25

research field:生物信息学传染病学基因组学重症医学临床微生物学

Abstract

Lower respiratory tract infections (LRTIs) frequently occur as a severe complication in intensive care unit (ICU) patients, substantially raising patient mortality rates and extending hospitalization periods. In this study, a retrospective cohort study of 261 suspected LRTI patients in the ICU of Nanjing Drum Tower Hospital between April 2021 and February 2024 was conducted. The results showed that metagenomic next-generation sequencing (mNGS) had a sensitivity of 80.1%, a specificity of 35%, and an accuracy of 66.3% across all samples. For pathogen detection, mNGS outperformed conventional microbiological testing (CMT) in detecting bacteria and DNA viruses, while CMT had a slight advantage in RNA virus detection, though the difference was not statistically significant ( p = 0.305). When comparing microbial profiles between survival and death groups, survivors had a more diverse pathogen spectrum, particularly in bacteria and RNA viruses. There were 262 species detected in both groups, with Corynebacterium striatum being the dominant species in the survival group and Pseudomonas aeruginosa in the death group. In the 128 patients whose treatment plans were adjusted based on mNGS results, 59.4% underwent escalation, 25.8% had their medications changed, and 1.6% initiated new treatment regimens. Further follow-up revealed that mNGS - guided treatment adjustments were effective in improving clinical symptoms in 58.6% of ICU patients. A predictive model for patient outcomes was developed utilizing the random forest algorithm, achieving an area under the receiver operating characteristic curve (AUC) of 0.722.

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