摘要
出身背景
早期识别病原体及其抗生素耐药性对于呼吸机相关性肺炎（VAP）患者的管理和治疗至关重要。然而，微生物培养可能耗时，并且对许多潜在病原体的培养能力有限。在这项研究中，我们开发了一种基于纳米孔的宏基因组下一代测序（mNGS）快速诊断方法，用于检测VAP病原体和抗微生物耐药性基因（ARGs）。
患者和方法
在2021年11月至2022年7月期间，采集了63名疑似VAP患者的气管内抽吸物（ETA）样本。建立受试者操作特征（ROC）曲线，以比较目标病原体读数、微生物读数百分比（RPM）和相对丰度（RA）的病原体鉴定性能。分别与金标准和复合标准进行了mNGS准确性的评估。然后，通过mNGS对ARGs进行分析。
后果
ROC曲线显示，RA具有最高的诊断价值，相应的阈值为9.93%。基于金标准，mNGS测试的敏感性和特异性分别为91.3%和78.3%，而基于复合标准，mNG测试的灵敏度和特异度分别为97.4%和100%。根据mNGS结果，共有13名患者呈病毒阳性，而与临床结果相比，合并感染率从27%增加到46%。mNGS测试在预测抗微生物耐药性表型方面也表现良好。与早发性VAP患者相比，晚发型VAP患者的呼吸道微生物组中ARGs的比例明显更高（P=0.041）。此外，mNGS的中位周转时间为4.43小时，而常规培养为72.00小时。
结论
在这项研究中，我们开发了一种工作流程，可以准确检测VAP病原体，并能够在mNGS收到样本后5小时内预测抗微生物耐药性表型。
Abstract
Background
The early identification of pathogens and their antibiotic resistance are essential for the management and treatment of patients affected by ventilator-associated pneumonia (VAP). However, microbiological culture may be time-consuming and has a limited culturability of many potential pathogens. In this study, we developed a rapid nanopore-based metagenomic next-generation sequencing (mNGS) diagnostic assay for detection of VAP pathogens and antimicrobial resistance genes (ARGs).

Patients and Methods
Endotracheal aspirate (ETA) samples from 63 patients with suspected VAP were collected between November 2021 and July 2022. Receiver operating characteristic (ROC) curves were established to compare the pathogen identification performance of the target pathogen reads, reads percent of microbes (RPM) and relative abundance (RA). The evaluation of the accuracy of mNGS was performed comparing with the gold standard and the composite standard, respectively. Then, the ARGs were analyzed by mNGS.

Results
ROC curves showed that RA has the highest diagnostic value and the corresponding threshold was 9.93%. The sensitivity and specificity of mNGS test were 91.3% and 78.3%, respectively, based on the gold standard, while the sensitivity and specificity of mNGS test were 97.4% and 100%, respectively, based on the composite standard. A total of 13 patients were virus-positive based on mNGS results, while the coinfection rate increased from 27% to 46% compared to the rate obtained based on clinical findings. The mNGS test also performed well at predicting antimicrobial resistance phenotypes. Patients with a late-onset VAP had a significantly greater proportion of ARGs in their respiratory microbiome compared to those with early-onset VAP (P = 0.041). Moreover, the median turnaround time of mNGS was 4.43 h, while routine culture was 72.00 h.

Conclusion
In this study, we developed a workflow that can accurately detect VAP pathogens and enable prediction of antimicrobial resistance phenotypes within 5 h of sample receipt by mNGS.

https://www.tandfonline.com/doi/full/10.2147/IDR.S397755