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

项目摘要/摘要 这项建议的目的是将基本的、流行病学的和技术的发现转化为新的 预防医疗保健相关感染和抗生素耐药性以及改善抗生素使用方式的策略 诊断测试在所有医疗保健设置中使用。这项工作包括六个核心项目和三个 可选的协作项目。核心项目包括：(1)使用人因工程学方法 改善各种长期护理环境中的环境清洁；(2)开发和测试临床医生- 成人和儿童知情的与抗生素相关的电子不良事件伤害评分，使用来自 改变医院抗生素处方行为的电子健康记录(EHR)；(3) 开发一种方法来检测顺序占用患者的生物体传播事件 使用来自电子病历的数据，并试行将其用作改善房间清洁的指标； (4)产超广谱β-内酰胺酶肠杆菌的分子流行病学特征， 预测哪些患者与他们一起定居，并确定感染控制失误和 抗生素在其传播过程中的暴露；(5)发育的新生儿的鼻部微生物区系特征 金黄色葡萄球菌菌血症了解如何预防这种致命的感染；以及(6)发展 使用电子病历数据的算法，以快速识别接触过 标明患有传染性疾病的患者，目标是迅速降低继发性疾病的风险 曝光。可选的合作项目包括：(1)制定血液培养基准和 血培养阳性率和血培养不适当的共识定义以及使用这些 结果实施和评估了一种循证算法，以改善多个国家的血液培养实践。 成人医学患者的机构队列；(2)表征与微生物学和地理学有关的风险 在医院外获得的中心线相关血流感染的模式(例如，在 在家中，在长期护理设施中，在透析中)，并试行预防工具包，以降低感染风险 出院时需要中心导尿管的患者，以及(3)确定出生后年龄如何影响 新生儿重症监护病房(NICU)新生儿出生体重与菌血症发生率的关系 开发一种实用的方法来报告NICU发病的菌血症比率，该方法根据出生体重和 出生后年龄。这项建议表明有能力整合医疗保健流行病学方面的专业知识 抗生素与其他学科的管理--人因工程、数据科学、机器学习、 微生物学、数学建模、微生物组科学和实施科学--目标是 确定预防医疗保健相关感染和抗生素耐药性的新方法，并可以 告知疾控中心指导并在多个环境中应用，以减少对患者和医疗保健的伤害 工人们。

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.