Pseudomonas aeruginosa-driven airway dysbiosis and machine learning prediction of acute exacerbations in non-cystic fibrosis bronchiectasis: a microbial-inflammatory signature approach
PA colonization disrupts airway microbial diversity and outcompetes commensal species in bronchiectasis, yet our XGBoost model reveals that ecological resilience-not pathogen load-best predicts exacerbation risk when integrated with inflammatory markers. This paradigm shift from pathogen-centric to ecosystem-driven risk assessment provides an actionable framework for personalized management and antibiotic stewardship in chronic airway diseases.
Authors
- Jian Xu (ORCID: https://orcid.org/0000-0002-2351-7403)
- Jin‐Fu Xu (ORCID: https://orcid.org/0000-0002-8039-8973)
- Yuhan Wang (ORCID: https://orcid.org/0000-0003-4447-5043)
- Yuanlin Song (ORCID: https://orcid.org/0000-0002-9826-4383)
- Wenwen Wang (ORCID: https://orcid.org/0009-0003-3568-510X)
Institutions
- Tongji University (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Zhongshan Hospital (CN)
- Shanghai Pulmonary Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- BMC Pulmonary Medicine
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1186/s12890-025-03892-7
- Primary Topic
- Cystic Fibrosis Research Advances
- Type
- article
- Field-Weighted Citation Impact
- 0.00