Identifying pre-sepsis opportunities for early, targeted intervention

PROJECT SUMMARY Sepsis kills an estimated 11 million people worldwide every year. Sepsis also contributes to as many as 50% of US hospital deaths. Early treatment is the only universally recognized modifiable factor for improving sepsis mortality. Thus, current treatment guidelines focus heavily on early sepsis care immediately following hospital presentation. However, today, little is known about even earlier, pre-hospital, opportunities to predict, recognize, or treat sepsis. Our research program uses innovative translational informatics approaches to elucidate the presentation, pace, and profile of infection in pre-sepsis patients that could enable novel pre- hospital approaches designed to mitigate, or even prevent, sepsis. These pre-sepsis opportunities are now being recognized as a key new frontier of sepsis research and care. In this renewal, we will extend successful research that has begun to identify and characterize pre-sepsis opportunities for early, targeted intervention. To achieve this goal, we will leverage granular electronic health record (EHR) data, advanced informatics and artificial intelligence/machine learning methods, and the design of novel pre-sepsis care programs. Through this proposal, we will rigorously test sepsis prediction models in key clinical subgroups, prospectively validate the performance of pre-sepsis prediction models in real-time EHR platforms, and assess the value of additional screening and diagnostic modalities for pre-sepsis risk stratification. Our findings will have broad and immediate implications for patients, clinicians, and health systems to address this devastating condition. These results will also inform the design of sepsis public health programs and future prospective interventional studies aimed at improving outcomes for a condition that remains common, deadly, and costly.