Sepsis kills an estimated 6 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, key national and global health authorities also emphasize the need to identify even earlier pre-hospital opportunities to predict, recognize, or treat sepsis. Little is known about the presentation, pace, and profile of infection in pre-sepsis patients. Understanding these characteristics could enable novel pre-hospital approaches designed to mitigate, or even prevent, sepsis. Using an innovative translational informatics approach, this project will identify and characterize pre-sepsis opportunities for early, targeted intervention. To achieve this goal, my laboratory leverages comprehensive electronic medical record data, advanced informatics methods, and the evaluation of novel care programs. Through this proposal, we will develop and validate prediction models that identify patients in common pre-hospital settings at the highest risk of impending sepsis, organ failure, and mortality. In the process, we will characterize the sepsis-related symptom profiles that portend the highest risk of adverse outcomes. Findings will have broad and immediate implications for patients, clinicians, and health systems. These results will also inform the design of sepsis public health programs and future randomized clinical trials aimed at improving outcomes for this devastating condition.
Sepsis kills 6 million people every year and early treatment is the key step for improving survival. We currently focus on treating people once they get to the hospital or emergency department, which is often too late. This study will identify opportunities to improve the care of patients who show worrisome signs of sepsis risk before they get to the hospital.
