Bacterial and Viral Respiratory Tract Microbiota and Host Characteristics in Adults With Lower Respiratory Tract Infections: A Case-Control Study

Background Viruses and bacteria from the nasopharynx are capable of causing community-acquired pneumonia (CAP), which can be difficult to diagnose. We aimed to investigate whether shifts in the composition of these nasopharyngeal microbial communities can be used as diagnostic biomarkers for CAP in adults. Methods We collected nasopharyngeal swabs from adult CAP patients and controls without infection in a prospective multicenter case-control study design. We generated bacterial and viral profiles using 16S ribosomal RNA gene sequencing and multiplex polymerase chain reaction (PCR), respectively. Bacterial, viral, and clinical data were subsequently used as inputs for extremely randomized trees classification models aiming to distinguish subjects with CAP from healthy controls. Results We enrolled 117 cases and 48 control subjects. Cases displayed significant beta diversity differences in nasopharyngeal microbiota (P = .016, R-2 = .01) compared to healthy controls. Our extremely randomized trees classification models accurately discriminated CAP caused by bacteria (area under the curve [AUC] .83), viruses (AUC .95) or mixed origin (AUC .81) from healthy control subjects. We validated this approach using a dataset of nasopharyngeal samples from 140 influenza patients and 38 controls, which yielded highly accurate (AUC .93) separation between cases and controls. Conclusions Relative proportions of different bacteria and viruses in the nasopharynx can be leveraged to diagnose CAP and identify etiologic agent(s) in adult patients. Such data can inform the development of a microbiota-based diagnostic panel used to identify CAP patients and causative agents from nasopharyngeal samples, potentially improving diagnostic specificity, efficiency, and antimicrobial stewardship practices. Classification models using a small set of nasopharyngeal bacteria, viruses, and clinical variables can be leveraged to accurately distinguish respiratory tract infections from health, with even better results when bacterial and viral infections were stratified.