Application of metagenomic next-generation sequencing in the diagnosis of pulmonary invasive fungal disease - Wang, Chengtan; You, Zhiqing; Fu, Juanjuan; Chen, Shuai; Bai, Di; Zhao, Hui; Song, Pingping; Jia, Xiuqin; Yuan, Xiaoju; Xu, Wenbin; Zhao, Qigang; Pang, Feng (Frontiers in Cellular and Infection Microbiology, 2022)
Background: Metagenomic next-generation sequencing (mNGS) is increasingly being used to detect pathogens directly from clinical specimens. However, the optimal application of mNGS and subsequent result interpretation can be challenging. In addition, studies reporting the use of mNGS for the diagnosis of invasive fungal infections (IFI)s are rare. Objective：We critically evaluated the performance of mNGS in the diagnosis of pulmonary IFIs, by conducting a multicenter retrospective analysis. The methodological strengths of mNGS were recognized and diagnostic cut-offs were determined. Methods: A total of 310 patients with suspected pulmonary IFIs were included in this study. Conventional microbiological tests (CMTs) and mNGS were performed in parallel on the same set of samples. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the logarithm of reads per kilobase per million mapped reads lg(RPKM), and read counts were used to predict true-positive pathogens. Result: The majority of the selected patients (86.5%) were immunocompromised. Twenty species of fungi were detected by mNGS, which was more than was achieved with standard culture methods. Peripheral blood lymphocyte and monocyte counts, as well as serum albumin levels, were significantly negatively correlated with fungal infection. In contrast, C-reactive protein (CRP) and procalcitonin (PCT) levels showed a significant positive correlation with fungal infection. ROC curves showed that mNGS (and especially lg[RPKM]) was superior to CMTs in its diagnostic performance. The area under the ROC curve (AUC) value obtained for lg(RPKM) in the bronchoalveolar lavage fluid (BALF) of patients with suspected pulmonary IFIs, used to predict true- positive pathogens, was 0.967 and the cut-off value calculated from the Youden index was -5.44. Conclusions: In this study, we have evaluated the performance of mNGS-specific indicators that can identify pathogens in patients with IFIs more accurately and rapidly than CMTs, which will have important clinical implications.