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

通过个性化的智能抗菌谱改善革兰氏阴性菌感染的经验性抗菌治疗

• 批准号: 10263255
• 负责人: Michihiko Goto
• 金额: $ 14.38万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10263255
• 关键词: Improving; Empiric; Antimicrobial; Therapy; Gram

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.

项目摘要/摘要 抗菌素耐药性增加（AMR）是最紧迫的公共卫生威胁之一。 2019年中心 对于疾病控制和预防（CDC）估计，AMR感染至少有280万人 并且在美国每年至少有35,900例过剩死亡。这种威胁尤其是 革兰氏阴性杆（GNR）病原体中有问题，其中高速耐药性 抗菌素在全球范围内出现，而我们开发新抗菌剂的努力却跌跌撞撞。 要减速GNR病原体中AMR的出现，必须引导临床医生远离 选择不必要的广谱抗菌素。一种抗体图，设施级别的摘要 抗菌敏感性数据是一种常见的局部参考工具，临床医生在选择经验时使用它 治疗。但是，抗体图有主要局限性。首先，关于临床医生目前的了解知之甚少 在做出经验疗法决策时使用它们。其次，抗体图数据是在设施级别汇总的， 数据可能会根据实践或地理区域的类型偏斜。最后，但最重要的是 抗体图未考虑任何患者级因素。因此，有强大的迫切需要 了解1）临床如何使用抗体图，2）抗体图反映了多少 针对个别患者的AMR和3）我们如何克服抗体图的局限性以优化经验疗法 并减少AMR。总体目标是创建一种小说，实时个性化的抗体图（“聪明 抗体图”），以克服当前的抗体图并优化经验治疗的临床选择 对于GNR，通过基于编码患者和患者的机器学习模型提供“ AMR的预测风险” 设施级数据。该目标将通过（a）健康信息学科学硕士实现 课程工作，（b）指导咨询委员会（c）精心选择的会议和讲习班，以及（d） 一项指导的研究。我们的具体目的是（1）表征当前在临床中使用抗体 练习并衡量临床医生做出经验疗法的决定时，可接受的抵抗风险 革兰氏阴性的血液感染和潜水员临床环境中的尿路感染； （2）评估 当前使用的抗体摄影的准确性可以预测大型患者的抗药性风险 GNR感染的回顾性微生物队列； （3）开发机器学习模型以预测 感染GNR病原体的患者AMR的个性化风险，并在前瞻性和外部验证。 这将导致未来的个性化决策支持工具（“智能抗体图”）的发展。这 该AHRQ K08奖的预期结果将是对有效性的全面理解 抗体图的局限性以及开发智能抗体图的信息包。在此K08的末尾 奖项，候选人将准备好成为拥有AMR专业知识的独立调查员 健康信息，在AMR预测模型决策支持系统中具有特定优势。