Situation Awareness to Improve Infant Sepsis Recognition in the Presence of Clinical Uncertainty

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PROJECT SUMMARY Sepsis has higher mortality in infants than other pediatric age groups, is associated with severe long- term morbidities in 30-50% of survivors, and burdens healthcare resources with prolonged hospitalization and complex interventions. Rapid identification of sepsis and timely initiation of antimicrobial therapy are critical to improve infant outcomes. However, limitations to current diagnostic approaches include the heterogeneous, subtle clinical presentation of infants and limited accuracy of laboratory tests. There is therefore an urgent need for strategies to improve the early detection of sepsis in infants to improve outcomes. Our objective is to improve sepsis recognition by developing an infant sepsis early recognition system that combines patient data with predictive model outputs to deliver timely, precise and relevant information to clinicians and nurses. Our hypothesis is that the integration of a predictive model with clinical data displays that improve situation awareness will improve timely sepsis recognition and management. We will utilize the strong foundation of our preliminary work in predictive modeling and existing data from our neonatal sepsis registry to produce novel methods to identify infants at greatest risk for neonatal sepsis. We have assembled a multi-disciplinary team of investigators from the disciplines of data science, clinical informatics, neonatology, and sepsis/infectious disease research to provide expert consensus recommendations. At the conclusion of the proposed work, we will have developed methods that support the integration of clinical data and machine learning outputs into decision support tools suited to clinical workflows. We anticipate such systems that pair advanced prediction methods with user-centered design processes will have broad applicability to many conditions and populations. This work will form the foundation for a future clinical trial to evaluate its ability to identify infants at highest risk of sepsis and provide clinicians and nurses with the decision support needed to improve their health and safety.