Principal Investigator/Program Director (Last, first, middle): Reddy, Jr., Bobby Project Summary: Sepsis is a poorly understood clinical syndrome characterized by a dysregulation of the immune system’s response to infection. It is the leading cause of death and is the most expensive condition treated in U.S. hospitals, exerting a $20.3 billion burden in 2011, 5.2% of total costs to the healthcare system nationwide. One of the major challenges facing clinicians is to identify and recognize patients with sepsis and impending organ dysfunction. The clinical manifestations of sepsis are highly variable and the signs of infection and organ dysfunction can be subtle and may mimic other conditions. Sepsis is also highly time critical. Every 1-hour delay in antibiotics after emergency department (ED) triage or the onset of organ dysfunction or shock is associated with a 3–7% increase in the odds of a poor outcome. These conditions have created an environment where physicians have to diagnose a complex, heterogeneous condition in a short timeframe with limited information. There is currently a dire need for a tool that can quickly assess if a patient is at risk for sepsis. Prenosis is a company focused on elucidating the complexity of dysregulated host response to infection. In partnership with 4 hospitals, we have built the world’s largest and most rapidly growing dataset & data-rich biobank that combine time series biomarker data with clinical data for patients suspected of infection in hospital environments. This dataset & biobank currently have >2,000 patients, >70,000 proprietary biomarker measurements, >1,200,000 Electronic Medical Record (EMR) parameters, and >25,000 samples banked (all with accompanying full time series EMR data). We currently have executed contracts for 6 total hospital partnerships, with the potential to expand the dataset by >65,000 patients per year if our pipeline were at full capacity. In this proposed project, Prenosis will finalize the first version of the NOSISTM platform by growing our current proprietary dataset & biobank from its current size of about 2,000 patients to over 10,000 total patients (Aim 1). Using the current 2,000 patient dataset, we have demonstrated initial promising endotypes of sepsis that could be useful for a variety of critical clinical problems. As we grow the dataset to 10,000 patients, we will use unsupervised machine learning algorithms trained on roughly half of the patients (5,000) to definitively prove the robustness and usefulness of these endotypes. The other half of the patients (other 5,000) will be used as a multi-site validation cohort for the endotypes determined by the ML algorithms (Aim 2). We will also finalize the actual software platform for the NOSISTM product (Aim 3), including data security, restricted access by collaborators to train and jointly develop products, and templates for business partnerships with potential collaborators (with an initial focus on HIT companies and p...