Integrated host-microbe biomarkers for early diagnostic classification of paediatric LRTI in mechanically ventilated children

Early diagnosis of paediatric lower respiratory tract infection (LRTI) in intensive care is difficult because clinical features overlap with non-infectious respiratory failure and conventional microbiology has limited sensitivity. We aimed to integrate airway microbial and host-response signals to identify early diagnostic biomarkers in mechanically ventilated children.

We re-analysed airway metagenomic sequencing and tracheal aspirate transcriptomics from 261 ventilated paediatric intensive care unit (PICU) patients using differential expression, protein-protein interaction network analysis, and machine-learning feature selection. Findings were evaluated in an exploratory assessment of biologically related cytokine markers within an independent prospective cohort (RASCALS; n = 100 enrolled, n = 78 analysed) using non-bronchoscopic mini-bronchoalveolar lavage (mini-BAL), plasma cytokines, and clinician-adjudicated diagnoses.

Respiratory syncytial virus and Haemophilus influenzae were the only pathogens consistently enriched in LRTI. Host transcriptomics showed activation of interferon, cytokine, and chemokine signalling. Network-guided feature selection identified a seven-gene panel (IRF7, FFAR3, GZMB, FABP4, FN1, CXCL5, BCAR1) with high diagnostic performance in cross-validation (median F1 0.94), comparable to a published 14-gene model. In RASCALS, a logistic model using mini-BAL IL-1β, IL-4, and IL-8 classified bacterial LRTI with 65% accuracy (sensitivity 70%, specificity 63%), while blood IL-6 and TRAIL achieved 82% accuracy (sensitivity 79%, specificity 83%).

Integrating airway microbial profiles with host-response biomarkers supports earlier and more specific LRTI diagnostic classification in ventilated children. The seven-gene panel and cytokine combinations are candidates for rapid PCR- or immunoassay-based bedside tests to inform antimicrobial stewardship.