Randomized trial investigates metagenomic and metabolomic differences in pneumonia severity in children, suggesting improved early diagnostics and treatment strategies.
Key Points
This study aims to identify distinct microbiome and metabolomic profiles between mild and severe Mycoplasma pneumoniae pneumonia in children. It seeks to enhance diagnostic accuracy and treatment strategies.
Prospective real-world study analyzing bronchoalveolar lavage fluid samples from 153 children.
Utilization of metagenomic sequencing and non-targeted metabolomic analysis.
Development and validation of a machine learning classification model for improved diagnostic accuracy.
Severe pneumonia group demonstrated significant changes in bacterial community composition, including coexistence of Mycoplasma pneumoniae and Alphainfluenzavirus influenzae.
Macrolide resistance in Mycoplasma pneumoniae exceeded 80% in severe cases, highlighting the need for careful antibiotic selection.
The machine learning model achieved an area under the curve of 0.909 to 0.991, surpassing traditional clinical methods.