背景:成人下呼吸道感染(LRTIs)因挑战传统检测方法的多种病原体而复杂化,通常是缓慢和麻木不仁的。宏基因组下一代测序(mNGS)提供了一个全面的,高通量,和无偏见的病原体鉴定方法。这项回顾性研究评估了mNGS与常规微生物测试(CMT)在LRTIs中的诊断功效,旨在提高检测准确性并实现早期临床预测。
方法:在我们的回顾性单中心分析中,从2020年7月至2023年7月,451名疑似LRTI患者接受了mNGS检测。我们评估了病原体谱,并比较了mNGS与CMT的诊断功效,以临床综合诊断为参考标准。该研究分析了疑似肺部感染病例的肺组织活检和支气管肺泡灌洗液(BALF)中的mNGS表现。根据临床结果(改善或死亡率)将患者分为两组,我们比较了两组之间的临床数据和常规实验室指标。使用单变量和多变量逻辑回归建立LRTI预后的预测模型和列线图,通过ROC曲线下面积(AUC)评估模型预测准确性。
结果:(1)mNGS与CMT的比较分析:在对510个标本的综合分析中,其中59例同时来自肺组织活检和BALF,该研究强调了mNGS相对于CMT的诊断优势。具体来说,mNGS在BALF样本中表现出显著更高的灵敏度和特异性(82.86%vs.44.42%和52.00%与21.05%,分别,p<0.001),同时具有更大的阳性和阴性预测值(96.71%vs.79.55%和15.12%5.19%,分别,p<0.01)。此外,在比较肺组织活检和BALF的同时测试时,mNGS在BALF中显示出增强的敏感性(84.21%vs.57.41%),而肺组织提供更高的特异性(80.00%vs.50.00%)。(2)本研究患者的感染性物种分析:该研究还注意到有关肺脓肿的发生率,并确定了EB病毒(EBV),具核梭杆菌,肺炎支原体,披肩衣原体,流感嗜血杆菌是最常见的病原体,肺炎克雷伯菌成为主要的细菌罪魁祸首。在疱疹病毒中,EBV和疱疹病毒7(HHV-7)是最常见的检测,HHV-7在免疫受损个体中更普遍。(3)LRTIs患者预后不良的危险因素及死亡风险预测模型:我们确定了下呼吸道感染患者预后不良的关键危险因素。具有重要的发现,包括延迟mNGS测试的时间,低淋巴细胞百分比,慢性肺病的存在,多种合并症,假阴性CMT结果,和疱疹病毒阳性影响患者预后。我们还开发了一个具有良好一致性和高准确性(AUC为0.825)的列线图模型,用于预测这些患者的死亡风险。为评估预后提供了有价值的临床工具。
结论:该研究强调mNGS是诊断下呼吸道感染的优越工具,比传统方法具有更高的灵敏度和特异性。
BACKGROUND: Lower respiratory tract infections(LRTIs) in adults are complicated by diverse pathogens that challenge traditional detection methods, which are often slow and insensitive. Metagenomic next-generation sequencing (mNGS) offers a comprehensive, high-throughput, and unbiased approach to pathogen identification. This retrospective study evaluates the diagnostic efficacy of mNGS compared to conventional microbiological testing (CMT) in LRTIs, aiming to enhance detection accuracy and enable early clinical prediction.
METHODS: In our retrospective single-center analysis, 451 patients with suspected LRTIs underwent mNGS testing from July 2020 to July 2023. We assessed the pathogen spectrum and compared the diagnostic efficacy of mNGS to CMT, with clinical comprehensive diagnosis serving as the reference standard. The study analyzed mNGS performance in lung tissue biopsies and bronchoalveolar lavage fluid (BALF) from cases suspected of lung infection. Patients were stratified into two groups based on clinical outcomes (improvement or mortality), and we compared clinical data and conventional laboratory indices between groups. A predictive model and nomogram for the prognosis of LRTIs were constructed using univariate followed by multivariate logistic regression, with model predictive accuracy evaluated by the area under the ROC curve (AUC).
RESULTS: (1) Comparative Analysis of mNGS versus CMT: In a comprehensive analysis of 510 specimens, where 59 cases were concurrently collected from lung tissue biopsies and BALF, the study highlights the diagnostic superiority of mNGS over CMT. Specifically, mNGS demonstrated significantly higher sensitivity and specificity in BALF samples (82.86% vs. 44.42% and 52.00% vs. 21.05%, respectively, p < 0.001) alongside greater positive and negative predictive values (96.71% vs. 79.55% and 15.12% vs. 5.19%, respectively, p < 0.01). Additionally, when comparing simultaneous testing of lung tissue biopsies and BALF, mNGS showed enhanced sensitivity in BALF (84.21% vs. 57.41%), whereas lung tissues offered higher specificity (80.00% vs. 50.00%). (2) Analysis of Infectious Species in Patients from This Study: The study also notes a concerning incidence of lung abscesses and identifies Epstein-Barr virus (EBV), Fusobacterium nucleatum, Mycoplasma pneumoniae, Chlamydia psittaci, and Haemophilus influenzae as the most common pathogens, with Klebsiella pneumoniae emerging as the predominant bacterial culprit. Among herpes viruses, EBV and herpes virus 7 (HHV-7) were most frequently detected, with HHV-7 more prevalent in immunocompromised individuals. (3) Risk Factors for Adverse Prognosis and a Mortality Risk Prediction Model in Patients with LRTIs: We identified key risk factors for poor prognosis in lower respiratory tract infection patients, with significant findings including delayed time to mNGS testing, low lymphocyte percentage, presence of chronic lung disease, multiple comorbidities, false-negative CMT results, and positive herpesvirus affecting patient outcomes. We also developed a nomogram model with good consistency and high accuracy (AUC of 0.825) for predicting mortality risk in these patients, offering a valuable clinical tool for assessing prognosis.
CONCLUSIONS: The study underscores mNGS as a superior tool for lower respiratory tract infection diagnosis, exhibiting higher sensitivity and specificity than traditional methods.