Sepsis, a diffuse inflammatory response to infection, occurs in over 450,000 cases per year in the US and frequently progresses to organ dysfunction and death. Although experimental studies using cells and animals have greatly improved our understanding of the pathophysiology of sepsis, there remains a remarkable paucity of biochemical and genetic data regarding the natural history of this important public health problem. In particular, there is a need for better markers of sepsis and outcome and a more rigorous evaluation of the complex relationships among the many genetic, inflammatory, and clinical factors that appear to influence the development and outcome of sepsis. Because pneumonia is the most common cause of sepsis, patients with this infection represent an excellent clinical model for studying sepsis in a relatively homogeneous population. We propose to study a large cohort of patients (n=2,703) with community-acquired pneumonia (CAP). Our study will be """"""""piggy-backed"""""""" onto a multicenter trial of alternative hospital quality improvement initiatives that is already funded and slated to begin enrolling patient's early in 2001. In addition to collecting detailed clinical data, we will carry out careful genetic analyses, focusing on allelic variations in the coding or noncoding regions of genes whose products are important in the expression and/or regulation of the inflammatory response. We will also obtain measurements over time of the plasma concentrations or cell surface expression of several key inflammatory molecules. We will determine the influence of specific polymorphisms on the development, course and outcome of pneumonia and sepsis. We will test whether genetic predisposition to an exuberant inflammatory response protects against infection yet also increases risk for adverse systemic effects and outcome. We will compare genetic data from patients with results obtained from a cohort of healthy controls (n=300). We will test several existing hypotheses regarding the association of circulating inflammatory molecules with outcome. We will use time-varying regression analyses and probabilistic networks to explore in new detail relationships among genetic polymorphisms and the inflammatory response in sepsis. Finally, we will construct and evaluate two sets of clinical decision tools: i.) clinical risk prediction rules that incorporate genetic and inflammatory response variables with existing clinical factors, and; ii.) a state-transition simulation model of the course of sepsis that allows time-dependent estimates of the effects of alternative treatment decisions. This study will generate: new and valuable information regarding existing lines of inquiry and laboratory investigation; new hypotheses arising from the use of time-dependent modeling; and new clinical decision tools that have immediate and practical value for designing clinical trials and improving patient care.
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