Improving Empiric Antimicrobial Therapy for Gram-Negative Infections through a Personalized Smart Antibiogram

项目摘要/摘要 不断增加的抗菌素耐药性(AMR)是最紧迫的公共卫生威胁之一。2019年，该中心 疾病控制和预防中心(CDC)估计，AMR感染至少影响280万人 在美国，每年至少有35,900人死于过度死亡。这种威胁尤其是 在革兰氏阴性杆菌(GNR)中存在问题，其中对末梢线的耐药率很高 全球出现了抗菌剂，而我们开发新抗菌剂的努力却步履蹒跚。 为了减缓AMR在GNR病原体中的出现，至关重要的是引导临床医生远离 选择不必要的广谱抗菌剂。一份抗菌图，一份设施级别的摘要 药敏数据，是临床医生选择经验性药物时常用的本地参考工具 心理治疗。然而，抗生素图谱有很大的局限性。首先，对临床医生目前的情况知之甚少。 在作出经验性治疗决策时使用它们。第二，在设施级别聚集抗菌谱数据， 而且，数据可能会根据实践类型或地理区域进行倾斜。最后，但最重要的是，一个 抗菌图谱没有考虑任何患者水平的因素。因此，有强烈而紧迫的需求来 了解临床医生如何使用抗菌图谱，2)抗菌图谱在多大程度上反映了 针对个别患者的AMR以及3)我们如何克服抗生素图谱的限制以优化经验性治疗 并降低AMR。总体目标是创建一种新颖的、实时的个性化抗菌图谱(Smart 以克服目前抗菌图谱的局限性，优化临床医生的经验性治疗选择 对于GNRs，通过基于结合了患者和 设施级数据。这一目标将通过以下方式实现：(A)卫生信息学硕士 课程安排：(B)导师关系咨询委员会；(C)精心挑选的会议和讲习班；(D) 有指导的研究性研究。我们的具体目标是(1)表征目前临床上使用的抗生素图谱。 当临床医生做出经验性治疗决定时，实践和测量可接受的耐药性风险 不同临床环境下革兰氏阴性血液感染和尿路感染；(2)评估 目前使用的抗生素图谱预测个体患者大范围耐药风险的准确性 追溯GNR感染的微生物学队列；(3)开发一个机器学习模型来预测 对感染GNR病原体的患者的AMR风险进行个体化，并进行前瞻性和外部验证。 这将导致未来个性化决策支持工具(智能抗菌图)的发展。这个 本次AHRQ K08奖的预期结果将是对有效性的全面了解 以及抗生素图谱的局限性，以及开发智能抗生素图谱的信息学工具包。在本K08的末尾 获奖后，候选人将做好充分准备，成为一名在AMR和 健康信息学，在AMR预测模型决策支持系统中具有特定的优势。

Project Summary/Abstract Increasing antimicrobial resistance (AMR) is one of the most urgent public health threats. In 2019, the Center for Diseases Control and Prevention (CDC) estimated that infections with AMR affect at least 2.8 million people and are associated with at least 35,900 excessive deaths annually in the US. This threat is particularly problematic among Gram-negative rod (GNR) pathogens in which high-rates of resistance to last-line antimicrobials have emerged globally, while our efforts to develop new antimicrobials have stumbled. To decelerate the emergence of AMR among GNR pathogens, it is essential to guide clinicians away from choosing unnecessarily broad-spectrum antimicrobials. An antibiogram, a facility-level summary of antimicrobial susceptibility data, is a common local reference tool which clinicians use when choosing empiric therapy. However, antibiograms have major limitations. First, little is known about how clinicians are currently using them when making empiric therapy decision. Second, antibiogram data is aggregated at the facility-level, and data may be skewed based on the type of practice or geographic area. Lastly, but most importantly, an antibiogram does not consider any patient-level factors. Therefore, there are strong, and pressing needs to understand 1) how an antibiogram is used by the clinician, 2) how much an antibiogram reflects the risk of AMR for individual patients and 3) how we can overcome limitations of antibiogram to optimize empiric therapy and reduce AMR. The overall goal is to create a novel, real-time personalized antibiogram (“Smart Antibiogram”) to overcome current limitations of antibiogram and to optimize clinician choice of empiric therapy for GNRs by providing a “predicted risk of AMR” based on a machine learning model incorporating patient- and facility-level data. This goal will be accomplished through (a) Master of Science in Health Informatics coursework, (b) a Mentorship Advisory Committee, (c) carefully selected conferences and workshops, and (d) a mentored research study. Our specific aims are to (1) Characterize the current use of antibiograms in clinical practice and measure the acceptable risk of resistance when clinicians make empiric therapy decisions for Gram-negative bloodstream infections and urinary tract infections within diverse clinical settings; (2) Assess the accuracy of currently-used antibiograms to predict the risk of resistance for individual patients in a large retrospective microbiology cohort for GNR infections; (3) Develop a machine learning model to predict the individualized risk of AMR for patients infected with GNR pathogens and validate prospectively and externally. This will lead to the future development of a personalized decision support tool (“Smart Antibiogram”). The expected outcomes of this AHRQ K08 Award will be the comprehensive understanding of the effectiveness and limitations of antibiogram, and the informatics toolkits to develop Smart Antibiogram. At the end of this K08 Award, the candidate will be well-prepared to become an independent investigator with expertise in AMR and health informatics, with specific strength in AMR prediction model decision-support system.