Scalable and Interoperable framework for a clinically diverse and generalizable sepsis Biorepository using Electronic alerts for Recruitment driven by Artificial Intelligence (short title: SIBER-AI)

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PROJECT SUMMARY Sepsis is a major health challenge worldwide that is associated with a significant risk of mortality. The key to improved outcomes in sepsis is earlier treatment once diagnosed, with delays in therapy being associated with worse outcomes. Sepsis is a heterogenous disease, and thus despite decades of research focused on various aspects of sepsis, there still remains much to be learned about the underlying mechanisms that result in differences in outcomes. The utilization of biorepositories gives investigators the opportunity to study different mechanisms of disease; however, it is imperative that we collect biospecimens early on in disease and at different time point in order to understand disease trajectory. Furthermore, there are opportunities within critical care research to diversify the patient population enrolled in studies in order to investigate disparities that occur in sepsis. Thus, the need to develop best practices and standard operation procedures are required that may serve as templates for establishing scalable and generalizable sepsis biorepositories. This proposal aims to 1) develop an integrated multi-modal clinical, physiologic, volatilomic, and multi-omic biorepository profile driven by a semi-autonomous screening algorithm to enrich sepsis phenotypes; 2)design and test novel methods of biospecimen collection among enriched sepsis populations in both ambulance and acute care hospital environments ; and 3)develop novel approaches to biorepository consent that match the clinical context of sepsis, maximize representativeness among patients, and enhance trust and engagement among patients and surrogate decision-makers.