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Background <p>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.</p> Methods <p>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; <i>n</i> = 100 enrolled, <i>n</i> = 78 analysed) using non-bronchoscopic mini-bronchoalveolar lavage (mini-BAL), plasma cytokines, and clinician-adjudicated diagnoses.</p> Results <p>Respiratory syncytial virus and <i>Haemophilus influenzae</i> 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%).</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Zongtai Wu,
  • Gehad Youssef,
  • Iain R. L. Kean,
  • Zhenguang Zhang,
  • John A. Clark,
  • Nazima Pathan,
  • Namshik Han

摘要

Background

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.

Methods

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.

Results

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%).

Conclusions

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.