Clinical Medicine Department, North Sichuan Medical College, Nanchong, China.
Laboratory of Allergy and Precision Medicine, Chengdu Institute of Respiratory Health, the Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China.
Front Cell Infect Microbiol. 2024 May 23;14:1385562. doi: 10.3389/fcimb.2024.1385562. eCollection 2024.
Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types.
We collected bronchoalveolar lavage fluid samples from 138 patients diagnosed with lower respiratory tract infections and conducted mNGS to characterize the lung microbiota. Employing various machine learning algorithms, we investigated the correlation of key bacteria in patients with concurrent bronchiectasis and developed a predictive model for hospitalization duration based on these identified key bacteria.
We observed variations in microbial communities across different age groups, diseases, and infection types. In the elderly group, exhibited the highest relative abundance, followed by and . and emerged as the dominant genera at the genus level in the younger group, while and were prevalent species. Within the bronchiectasis group, dominant bacteria included , , and . Significant differences in the presence of were noted between the bronchiectasis group and the control group. In the group with concomitant fungal infections, the most abundant genera were and , with and as the predominant species. Notable differences were observed in the presence of , , , , and between the group with concomitant fungal infections and the bacterial group. Machine learning algorithms were utilized to select bacteria and clinical indicators associated with hospitalization duration, confirming the excellent performance of bacteria in predicting hospitalization time.
Our study provided a comprehensive description of the microbial characteristics among patients with lower respiratory tract infections, offering insights from various perspectives. Additionally, we investigated the advanced predictive capability of microbial community features in determining the hospitalization duration of these patients.
下呼吸道感染是常见病症。然而，目前对于下呼吸道微生物生态系统的理解仍不完整，需要进一步全面评估。借助宏基因组下一代测序（mNGS）技术的进步和机器学习的出现，现在可以比较不同年龄组、疾病和感染类型的患者下呼吸道微生物群落的特征。
我们收集了 138 例下呼吸道感染患者的支气管肺泡灌洗液样本，并进行 mNGS 以描述肺部微生物组。我们使用各种机器学习算法，研究了同时患有支气管扩张症的患者中关键细菌的相关性，并基于这些鉴定的关键细菌开发了住院时间的预测模型。
我们观察到不同年龄组、疾病和感染类型的微生物群落存在差异。在老年组中，相对丰度最高的是 ，其次是 和 。在年轻组中，属水平的优势属是 和 ，而优势种是 和 。在支气管扩张症组中，优势菌包括 、 、 。支气管扩张症组与对照组之间 的存在存在显著差异。在伴有真菌感染的组中，最丰富的属是 和 ，优势种是 和 。伴有真菌感染的组与细菌组之间 的存在存在显著差异。我们使用机器学习算法选择与住院时间相关的细菌和临床指标，证实了细菌在预测住院时间方面的出色表现。
我们的研究全面描述了下呼吸道感染患者的微生物特征，从多个角度提供了见解。此外，我们研究了微生物群落特征在确定这些患者住院时间方面的先进预测能力。
