Machine learning to map microbiome changes in ventilator-associated pneumonia
Official title: Machine Learning Approaches for Analyzing Microbiome Dynamics in Ventilator-Associated Pneumonia
Funded at University of New Haven · NIH-11424131 · Data updated 2026-11-12
A machine-learning guided study will analyze 16S microbiome data from mechanically ventilated patients to help identify and diagnose ventillator-associated pneumonia earlier.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of New Haven NIH-funded |
| Lab location | 1 site (West Haven, United States) |
| Project start | September 1, 2026 |
| Project end | August 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $126,351 |
| NIH project number | 1R16AI201229-01 |
| Project ID | NIH-11424131 on NIH RePORTER |
Who could benefit from this research
✓Could help
Critically ill, mechanically ventilated adults at risk of VAP who have 16S microbiome data and clinical information available.
✕Less likely to help
Patients without mechanical ventilation or without access to relevant microbiome/clinical data may not benefit from this research.
What this research studies
This project uses longitudinal 16S sequencing data from ICU patients on ventilators and combines them with clinical information to build interpretable models that can flag VAP risk earlier than current tests. Researchers will apply machine learning and genetic algorithms to find patterns in microbial communities, not just single pathogens, that differ between patients who develop VAP and those who do not. The work includes data from hundreds of patients across two institutions for both development and external validation. If successful, the approach could shorten time to diagnosis and improve antibiotic decisions. The study will start with existing datasets and mature toward clinically applicable decision-support tools.
Why it matters
Potential benefit: If successful, clinicians could diagnose VAP faster and tailor treatments sooner, potentially reducing mortality and ICU stays.
How similar studies have gone: Previous studies show that dynamic microbiome patterns can be associated with disease processes, but this project aims to translate that into actionable diagnostic tools for VAP.
Where this research is happening
-
University of New Haven
West Haven, United States Active
Researchers
- Principal investigator: Sayed, Khaled — University of New Haven
- Study coordinator: Sayed, Khaled
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.