PROJECT ABSTRACT Sepsis is a devastating syndrome that represents a leading cause of death, morbidity, and healthcare costs. Its impact is amplified by rising rates of antimicrobial resistance. Improving sepsis outcomes primarily results from prescribing timely antibiotics based on the estimated risk of multidrug resistance (MDR). Previous models grossly overestimated the MDR risk and exacerbated the escalating rates of antimicrobial resistance and excess mortality. The overall goal of this proposed K08 research is to identify common sepsis phenotypes that will enable better prescribing practices and standardized comparisons across hospitals, which will help practicing clinicians, researchers, healthcare institutions, and policy makers. These themes correlate with NIGMS's interest in finding innovative methods and leveraging big data to improve sepsis outcomes. Our three specific aims reflect these goals: (1) establish resistance thresholds for MDR Gram- negative bacilli (GNB) that cause sepsis, (2) assess the impact of sepsis definition on the performance of risk prediction models for MDR GNB, and (3) identify sepsis phenotypes at high risk for MDR GNB in a well-balanced cohort and assess the impact of case mix on risk prediction model performance. We will mathematically derive resistance thresholds that link population resistance rates to individual patient risk of death in sepsis caused by MDR GNB, assess factors that impact prediction performance, and incorporate rich clinical data from 15 hospitals in our healthcare system to identify stable common sepsis phenotypes. Dr. Vazquez Guillamet has training in Infectious Diseases and Critical Care Medicine and experience in antimicrobial resistance in critically ill patients. This proposal will build on her clinical work and previous research experience in finding innovative methods to solve challenging problems at the intersection of infectious diseases and critical care medicine. Dr. Vazquez Guillamet has six career objectives: (1) pursue advanced training in clinical epidemiology; (2) acquire skills in advanced linear regression and multilevel modeling; (3) learn supervised machine learning methods; (4) acquire skills in big data management in healthcare and methods to handle missing data; (5) improve scientific communication, grantsmanship, and leadership, and (6) participate in training in the responsible conduct of research. She will achieve these goals through didactic coursework, hands-on research experience, and active mentoring from experts in Infectious Diseases, Critical Care Medicine, and applied clinical informatics. She will continue to develop innovative methods to mitigate the antimicrobial resistance crisis, especially in critically ill patients, and become an analytics translator at the intersection of clinical medicine and clinical applied informatics. The fertile research environment at Washington University in St. Louis, the experienced mentorship team, and a well-crafted career development plan will enable Dr. Vazquez Guillamet to achieve her long-term goal of becoming an independently funded clinician-investigator utilizing big data to develop applications for risk prediction, surveillance, and outcome comparisons in antimicrobial resistance and sepsis.

PROJECT NARRATIVE The significant burden of sepsis augmented by the escalating rates of antimicrobial resistance has incited many national performance improvement initiatives. Research efforts have focused on predicting patients at risk for infections caused by multidrug resistant pathogens but existing models have led to risk overestimation thus promoting indiscriminate antimicrobial use and further fueling the emergence of resistance. The candidate, Dr. Vazquez Guillamet has focused on innovative ways to assess the impact of antimicrobial resistance and to develop generalizable useful risk prediction models. The current proposed research expands her previous work with the overall goal to develop clinical decision support tools that will improve antibiotic prescribing practices and will allow valid, case-mix adjusted comparisons across hospitals in terms of resistance prevalence rates and antimicrobial use.