Johns Hopkins Prevention Epicenter: Transdisciplinary Research Approaches to Prevent Healthcare Associated Infections and Antibiotic Resistance (TRAP HAI & AR)

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PROJECT SUMMARY/ABSTRACT The purpose of this proposal is to translate basic, epidemiologic, and technologic discoveries into new strategies to prevent healthcare-associated infections and antibiotic resistance and to improve how antibiotics and diagnostic tests are used across all healthcare settings. This work includes six Core Projects and three Optional Collaborative Projects. The Core Projects include: (1) using a human factors engineering approach to improve environmental cleaning in diverse long-term care settings; (2) developing and testing a clinician- informed, electronic antibiotic-associated adverse event harm score for adults and children using data from the electronic health record (EHR) with the goal of changing antibiotic prescribing behaviors in the hospital; (3) developing a measure to detect episodes of organisms transmission from patients sequentially occupying the same hospital room using data from the EHR and piloting its use as an indicator for improved room cleaning; (4) characterizing the molecular epidemiology of extended spectrum β-lactamase-producing Enterobacterales, predicting what patients are colonized with them, and determining the role of infection control lapses and antibiotic exposure in their transmission; (5) characterizing the nasal microbiota of neonates that develop Staphylococcus aureus bacteremia to understand how to prevent this deadly infection; and (6) developing an algorithm using EHR data to identify rapidly healthcare workers and patients who have been exposed to an index patient with a transmissible infectious disease with the goal of quickly mitigating the risk of secondary exposures. The Optional Collaborative Projects include: (1) developing a benchmark for blood culture and blood culture positivity rates and a consensus definition of blood culture inappropriateness and using these results to implement and evaluate an evidence-based algorithm to improve blood culture practices in a multi- institution cohort of adult medicine patients; (2) characterizing the risk for and microbiology and geographical patterns of central line-associated bloodstream infections that are acquired outside of the hospital (e.g., at home, in long-term care facilities, at dialysis) and pilot a prevention toolkit to reduce the risk of infection for patients requiring a central catheter upon hospital discharge, and (3) determining how postnatal age impacts the association between birthweight and bacteremia rates in the neonatal intensive care unit (NICU) in order to develop a practical approach for reporting NICU-onset bacteremia rates that adjusts for birthweight and postnatal age. This proposal demonstrates capacity to integrate expertise in healthcare epidemiology and antibiotic stewardship with other disciplines—human factors engineering, data science, machine learning, microbiology, mathematical modeling, microbiome science, and implementation science—with the goal of identifying novel approaches to prevent healthcare associated infections and antibiotic resistance and that can inform CDC guidance and be applied across multiple settings to reduce harm to patients and healthcare workers.