Associations of Mycoplasma pneumoniae load, co-infections, and macrolide resistance with clinical-laboratory profiles in hospitalized pediatric pneumonia: A targeted next-generation sequencing(tNGS) study of bronchoalveolar lavage fluid (BALF)
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Purpose This study aimed to investigate the associations of Mycoplasma pneumoniae (MP) DNA load, co-infection, and macrolide resistance with clinical phenotypes in pediatric pneumonia, using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid (BALF) Methods We conducted a retrospective cohort study of 791 hospitalized children with MP pneumonia. All patients underwent bronchoscopy, and bronchoalveolar lavage fluid (BALF) was analyzed using targeted next-generation sequencing (tNGS). This allowed for the simultaneous quantification of MP DNA load, comprehensive co-infection profiling, and detection of macrolide resistance mutations (A2063G/A2064G in 23S rRNA). Clinical data were correlated with these multidimensional tNGS parameters. Results Our analysis revealed distinct clinical phenotypes linked to tNGS findings. A high MP DNA load was associated with a "classic" phenotype of persistent fever and lung consolidation, typically in monoinfection. Conversely, a low MP DNA load served as a key marker for a co-infection-driven syndrome in younger children, characterized by more severe respiratory symptoms (wheezing, dyspnea) and systemic inflammation. Furthermore, macrolide-resistant MP (MRMP) was the strongest independent predictor of treatment failure, tripling the odds of requiring second-line antibiotics, without influencing initial disease severity. Conclusion This study demonstrates that the integration of MP DNA load, co-infection status, and resistance profiling from BALF-based tNGS provides a powerful framework for stratifying pediatric MP pneumonia. These multidimensional data offer critical insights for anticipating disease course and tailoring therapeutic strategies, paving the way for more personalized and effective patient management.