[R21] Clinical Trajectory-Based Modeling of C. difficile to Link Individual Risk Profiles to Healthcare Burden
Ente: National Institute of Allergy and Infectious Diseases
Scadenza: 2028-07-31
Importo max: 246.000 EUR
Paese: US
Descrizione
PROJECT SUMMARY
C. difficile infection (CDI) remains one of the most burdensome healthcare-associated infections, causing sub-
stantial morbidity, mortality, and healthcare costs. Most analytic frameworks treat CDI as a single event that
consists only of symptomatic infection. This fails to capture the full clinical progression including asymptomatic
colonization, heterogeneity in severity, persistent carriage after treatment, and relapse. By oversimplifying CDI’s
multistage natural history, all-or-none models can bias estimates of factors associated with disease progression,
and obscure opportunities to identify and rigorously evaluate targeted infection-control strategies.
To address these gaps, we propose a Hidden Markov modeling framework that explicitly integrates latent and
observable CDI states using patient-level electronic health data. Our approach combines latent-state inference
with regression-based transition rates. This enables modeling of individual clinical trajectories and response to
control interventions.
• Aim 1: Quantify patient-level drivers of CDI progression by developing and parameterizing a multistage Hidden
Markov model to determine how clinical characteristics such as antibiotic exposures influence transitions across
disease stages.
• Aim 2: Evaluate how interventions (e.g., antibiotic stewardship, prophylaxis, and contact precautions) affect
individual incidence, severity, relapse, persistent colonization.
• Aim 3: Project the population-level impact of interventions by incorporating stages of CDI progression into a
stochastic, covariate-dependent compartmental model. Indivdual- and population-level effect will be compared
by simulating clinical trajectories, transmission dynamics and system-wide burden under different prevention
strategies.
Expected outcome and impact: This project will improve our understanding of the natural history of the disease,
CDI risk stratification, and intervention effectiveness. This will enable data-driven clinical and policy decisions,
supporting more precise antimicrobial stewardship, screening, prophylaxis, and infection-control measures to
reduce CDI incidence, recurrence, and healthcare costs.
Istituzione: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
PI: Seth Blumberg, Daniel De la Rosa Martinez
Progetto: 1R21AI202888-01
Settori: National Institute of Allergy and Infectious Diseases
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