Improving Suspected Pulmonary Infection Diagnosis by Bronchoalveolar Lavage Fluid Metagenomic Next-Generation Sequencing: a Multicenter Retrospective Study.
Improving Suspected Pulmonary Infection Diagnosis by Bronchoalveolar Lavage Fluid Metagenomic Next-Generation Sequencing: a Multicenter Retrospective Study.
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通过支气管肺泡灌洗液宏基因组新一代测序提高疑似肺部感染的诊断：一项多中心回顾性研究。
DOI：
10.1128/spectrum.02473-21
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发表时间：
2022-08-31
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
宏基因组下一代测序（mNGS）因其无偏的病原体检测特性，已逐渐应用于临床实践。但其在疑似肺部感染中的诊断性能和临床价值尚需进一步评价。我们系统回顾了2019年1月至2021年9月来自4家医疗机构的246例疑似肺部感染患者的临床资料。以支气管肺泡灌洗液（BALF）为基础，系统分析mNGS与常规检查（CT）的诊断性能。还评估了mNGS和CT对诊断修改和治疗调整的影响。mNGS和CT的阳性率分别为47.97%和23.17%。mNGS的敏感性明显高于CT（53.49%vs23.26%，P < 0.01），尤其是结核分枝杆菌感染（67.86%对17.86%，P < 0.01），非典型病原体（100.00%比7.14%，P < 0.01）、病毒（92.31%比7.69%，P < 0.01）和真菌（78.57%比39.29%，P < 0.01）。mNGS的特异性上级优于CT（90.32%vs77.42%，P = 0.167）。mNGS的阳性预测值（PPV）为97.46%，阴性预测值（NPV）为21.88%。仅根据mNGS结果修改诊断32例（32/246，13.01%），调整治疗23例（23/246，9.35%）。mNGS可明显提高可疑肺部感染的诊断率，尤其是M.肺结核、非典型病原体、病毒和真菌，显示了肺部感染的病原体分布。有望成为一种有前途的微生物检测和诊断方法。重要性肺部感染是一种异质性和复杂的感染性疾病，在世界范围内发病率和死亡率都很高。在临床实践中，相当一部分肺部感染的病原学尚不清楚，微生物学诊断具有挑战性。宏基因组新一代测序以无偏倚的方式检测样品中的所有核酸，揭示微生物群落环境和生物体，并改善临床环境中感染性疾病的微生物检测和诊断。本研究是第一项完全基于BALF的mNGS病原体检测的多中心、大规模回顾性研究，证明了mNGS在疑似肺部感染的微生物检测和诊断方面的上级性能，尤其是在结核分枝杆菌、非典型病原体、病毒和真菌感染方面。它还展示了真实的世界中肺部感染的病原体分布，指导针对性治疗，提高临床管理和预防。
Metagenomic next-generation sequencing (mNGS) has been gradually applied to clinical practice due to its unbiased characteristics of pathogen detection. However, its diagnostic performance and clinical value in suspected pulmonary infection need to be evaluated. We systematically reviewed the clinical data of 246 patients with suspected pulmonary infection from 4 medical institutions between January 2019 and September 2021. The diagnostic performances of mNGS and conventional testing (CT) were systematically analyzed based on bronchoalveolar lavage fluid (BALF). The impacts of mNGS and CT on diagnosis modification and treatment adjustment were also assessed. The positive rates of mNGS and CT were 47.97% and 23.17%, respectively. The sensitivity of mNGS was significantly higher than that of CT (53.49% versus 23.26%, P < 0.01), especially for infections of Mycobacterium tuberculosis (67.86% versus 17.86%, P < 0.01), atypical pathogens (100.00% versus 7.14%, P < 0.01), viruses (92.31% versus 7.69%, P < 0.01), and fungi (78.57% versus 39.29%, P < 0.01). The specificity of mNGS was superior to that of CT, with no statistical difference (90.32% versus 77.42%, P = 0.167). The positive predictive value (PPV) and negative predictive value (NPV) of mNGS were 97.46% and 21.88%, respectively. Diagnosis modification and treatment adjustment were conducted in 32 (32/246, 13.01%) and 23 (23/246, 9.35%) cases, respectively, according to mNGS results only. mNGS significantly improved the diagnosis of suspected pulmonary infection, especially infections of M. tuberculosis, atypical pathogens, viruses, and fungi, and it demonstrated the pathogen distribution of pulmonary infections. It is expected to be a promising microbiological detection and diagnostic method in clinical practice. IMPORTANCE Pulmonary infection is a heterogeneous and complex infectious disease with high morbidity and mortality worldwide. In clinical practice, a considerable proportion of the etiology of pulmonary infection is unclear, microbiological diagnosis being challenging. Metagenomic next-generation sequencing detects all nucleic acids in a sample in an unbiased manner, revealing the microbial community environment and organisms and improving the microbiological detection and diagnosis of infectious diseases in clinical settings. This study is the first multicenter, large-scale retrospective study based entirely on BALF for pathogen detection by mNGS, and it demonstrated the superior performance of mNGS for microbiological detection and diagnosis of suspected pulmonary infection, especially in infections of Mycobacterium tuberculosis, atypical pathogens, viruses, and fungi. It also demonstrated the pathogen distribution of pulmonary infections in the real world, guiding targeted treatment and improving clinical management and prognoses.
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影响因子：
5.7
作者：
Li H;Gao H;Meng H;Wang Q;Li S;Chen H;Li Y;Wang H
通讯作者：
Wang H
影响因子：
4.4
作者：
Katz SE;Williams DJ
通讯作者：
Williams DJ
DOI：
10.1056/nejmoa1306801
发表时间：
2014-03-27
期刊：
The New England journal of medicine
影响因子：
--
作者：
Magill SS;Edwards JR;Bamberg W;Beldavs ZG;Dumyati G;Kainer MA;Lynfield R;Maloney M;McAllister-Hollod L;Nadle J;Ray SM;Thompson DL;Wilson LE;Fridkin SK;Emerging Infections Program Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey Team
通讯作者：
Emerging Infections Program Healthcare-Associated Infections and Antimicrobial Use Prevalence Survey Team
影响因子：
11.8
作者：
Miao, Qing;Ma, Yuyan;Hu, Bijie
通讯作者：
Hu, Bijie
DOI：
10.1164/rccm.200406-781oc
发表时间：
2004-12-01
影响因子：
24.7
作者：
Garbino, J;Gerbase, MW;Kaiser, L
通讯作者：
Kaiser, L