Integrating intestinal microbiome and urinary metabolome data to predict secondary infection in critically ill patients
Authors' conclusion
SI susceptibility in critically ill patients associates with underlying clinical severity, prior antibiotic exposure, and microbiota disruption. Multi-omics integration yielded consistent predictive improvement
Study context
- Population
- Mechanically ventilated critically ill patients, University Hospital Cologne (UHC), Germany, and Columbia University Medical Center (CUMC), New York, US64 UHC and 24 CUMC
- Participants
- 88 participants
- Comparison
- Integrated multi-omics models vs clinical-only models for secondary infection (SI) prediction
- Outcome
- Prognostic discrimination for secondary infection (SI: new-onset bloodstream infection or ventilator-associated pneumonia (VAP) ≥ 48 h after enrollment)
Methodological detailstwo cohorts, multi-omics integration
- Two cohorts
- Multi-omics integration
Performance
As reported in the abstract
Discrimination for SI
| Test or model | Value | AUC |
|---|---|---|
| Multi-omics | 0.75 | 0.75 |
| Clinical-only | 0.64 | 0.64 |
Value
Multi-omics0.75
Clinical-only0.64
AUC
Multi-omics0.75
Clinical-only0.64
Further findings
| Outcome | Odds ratio | P value |
|---|---|---|
| Prior antibiotic exposure, SI vs no SI84% vs. 41%, q < 0.01 | 2.57 | P = 0.17 |
SI incidence
- SI occurred
- 28%
Baseline intestinal microbial diversity (Shannon), SI vs no SI
- Lower in SI patients: 1.96 vs. 3.47
- P < 0.01
Enterococcus abundance, SI vs no SI
- Greater in SI patients: 46% vs. 11%
- q = 0.02
- Similar patterns in respiratory microbiome
Urinary nuclear magnetic resonance (NMR) metabolites
- Elevated in SI: 0.935 ppm (2-oxoisocaproate, isoleucine) in core dataset; 8.025 ppm (quinolinate) in UHC subset
Original publication
Integrating intestinal microbiome and urinary metabolome data to predict secondary infection in critically ill patients. Crit Care. Published 13 March 2026. doi:10.
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