Sepsis is a common condition with significant morbidity, mortality and annual costs of care in the billions of dollars. Despite innumerable studies on the causes of, and therapies for, sepsis, the mortality rate has not changed substantially in the last 20 years. Treatments remain generic, with current guidelines recommending the same approach for all patients, regardless of the litany of differences that exist at baseline. Moreover, the blanket administration of 30 cc/kg of intravenous fluid (IVF) to all patients is recognised as being directly harmful to some. Patient-level heterogeneity in prior sepsis trials is recognised as a substantial contributor to all these problems, yet no prior investigation has attempted to identify volume-informed septic phenotypes, a necessary first step towards precision care.
Predicated on prior studies demonstrating detectability of organ-level congestion, we hypothesise that central venous hypertension (1) is deleterious to the function of the lungs, liver, kidneys and vascular endothelium; (2) is worsened by cardiac dysfunction and IVF administration; and (3) contributes to adverse organ-specific and overall outcomes. Beginning in the emergency department, cardiac function will be assessed with echocardiography while congestion in the lungs and kidneys will be assessed using previously validated sonographic markers of congestion. Biomarkers for each organ will be collected concurrently, thereby increasing the fidelity of our phenotypic profiles by pairing indicators of macroscopic and microscopic stress and dysfunction. Data will also be collected at 24 hours and 7 days (or discharge, whichever comes first) after presentation. Classical and machine learning approaches will be used to analyse our large data stream and develop a rule-based system to identify distinct subpopulations of patients with sepsis who have greater risk/likelihood of both organ-specific and overall adverse outcomes.
This project has been approved by the Wayne State University Institutional Review Board, with patient enrolment beginning in April 2024. Findings will be reported and disseminated via conference presentations and open-access publications.

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