Project Grant R16AI201229

The National Institute of Allergy and Infectious Diseases awarded University of New Haven, Incorporated $126,351 on September 1, 2026, under the Allergy and Infectious Diseases Research program (CFDA 93.855) to develop machine learning approaches for analyzing microbiome dynamics in ventilator-associated pneumonia.

The project addresses VAP diagnostic delays and poor sensitivity in current clinical methods by developing interpretable machine learning models that integrate 16S rRNA sequencing data with clinical parameters to support VAP diagnosis. Aim 1 builds computational models using longitudinal microbiome data from 479 University of Pittsburgh Medical Center patients, with external validation on 83 University of Pennsylvania patients. Aim 2 employs genetic algorithms and state-space modeling to identify microbial taxa groups exhibiting collective dynamic trajectories associated with VAP development, potentially revealing early deviation points preceding clinical manifestation. The analytical framework incorporates Dirichlet mixture models, probabilistic graphical models with Markov blanket analysis, and novel genetic algorithm approaches for temporal pattern recognition. Expected outcomes include validated computational frameworks for temporal microbiome analysis and proof-of-concept evidence that coordinated microbial community shifts, integrated with clinical parameters, can identify VAP risk earlier and more accurately than existing methods.

Performance takes place in West Haven, Connecticut, with a period of performance from September 1, 2026, through August 31, 2030.