Comparative of metagenomic and targeted next-generation sequencing in lower respiratory tract fungal infections
Zhiyang Chen, Xin Liu, Tan Li, Xing Lyu, Qichen Long, Weimin Wu, Zhe Guo, Zhenni Liu, Ziyang Li, Min Hu
Abstract
Objectives This study aims to compare the diagnostic efficiency and consistency of metagenomic next-generation sequencing (mNGS) and targeted next-generation sequencing (tNGS) in patients with lower respiratory tract fungal infections. Methods A total of 115 patients with probable pulmonary infection between September 2022 and April 2023 were enrolled at the Second Xiangya Hospital, Changsha, China, of which 61 were clinically diagnosed with invasive pulmonary fungal infection (IPFI) and 54 were non-IPFI cases. All patients received bronchoalveolar lavage, with mNGS, tNGS, and cultures being conducted paralleled. Diagnostic effectiveness and consistency in detecting microorganisms were compared. Results Both mNGS and tNGS showed high sensitivity rates of 95.08% each, with specificity of 90.74% and 85.19%, respectively. They also demonstrated positive predictive values (PPVs) of 92.1% and 87.9% and negative predictive values (NPVs) of 94.2% and 93.9%, respectively, in diagnosing IPFI. The sensitivity and NPV of mNGS and tNGS were superior to that of any individual or combined conventional microbiological tests (CMTs) (P < 0.05). The consistency of culture with mNGS and tNGS was 48.70% and 50.43%, respectively. For fungal detection, Pneumocystis jirovecii (26/61, 42.6%; and 28/61, 45.9%), Candida albicans (19/61, 31.1%; and 21/61, 34.4%), and Aspergillus fumigatus (16/61, 26.2%; and 15/61, 24.6%) are most prevalent for mNGS and tNGS in enrolled cases, and the detection rate was greatly higher than that of culture. Furthermore, mNGS and tNGS were capable of diagnosing mixed infections in 65 and 55 out of the 115 cases, whereas only nine cases of bacterial-fungal infection were detected by culture. Conclusion The diagnostic efficacy of mNGS and tNGS was comparable to that of identified IPFI. NGS-based methodologies present a promising tool for detecting IPFI, which can be a good supplement to CMT.