Improving clinical, operational and economic outcomes related to MRSA

描述（由申请人提供）：耐甲氧西林金黄色葡萄球菌（MRSA）是许多医院获得性感染（HAIs）的原因。除了感染的临床负担外，mrsa定殖患者数量的增加对患者的预后和资源利用也有深远的影响，这一点尚未得到充分的研究和解决。mrsa定植的患者在入院时采取接触预防措施，可能会经历更长的等待时间来分配医院床位，减少与提供者的互动，更多可预防的不良事件，以及增加抗生素使用不当的风险和对护理的不满。临床医生和政策制定者可能会考虑几种策略来解决这个问题，每种策略都有临床和资源权衡。应用数学建模技术，特别是离散事件模拟技术（DES）在感染控制研究中的价值尚未得到充分认识。这个K01应用程序提出了三个假设驱动的目标，以推进感染控制、医院流行病学和抗菌素耐药性领域：1)估计定植史与抗菌素处方、床位分配时间和院内患者转移之间的关系，使用一个新的患者数据仓库；2)设计并验证三级医院患者流量的DES模型；3)应用验证的DES模型评估不同感染控制策略的临床和资源利用效果。这项创新的多学科研究将通过对相互竞争的筛查方法的权衡进行量化，为临床医生和政策制定者提供有价值的信息。我是马萨诸塞州总医院的一名医生，也是哈佛医学院的一名医学讲师，接受过传染病方面的培训，拥有卫生政策/经济学博士学位。我设计并实施了两项MRSA监测和停止接触预防领域的临床研究，以及两项关于接触预防影响的全国性调查。我将利用K奖扩展我目前的技能集，包括大型数据库的分析和管理，数学建模（特别是离散事件模拟）和优化方法。我的导师是David Hooper博士，传染病临床医生和感染控制和抗微生物药物耐药性领域的专家，以及Rochelle Walensky博士，传染病临床医生和数学建模师。我将与生物统计学、运筹学和信息系统领域的专家合作。有针对性的教育课程、指导计划和研究策略将确保我在获奖期间成功过渡到一名独立研究者，拥有使用建模技术评估感染控制方法和在临床环境中实施最佳策略的专业知识。我将利用从建模中收集到的有价值的信息来设计严谨的研究，以推进感染控制、医院流行病学和抗菌素耐药性领域的研究。

DESCRIPTION (provided by applicant): Methicillin-resistant Staphylococcus aureus (MRSA) is the cause of many hospital-acquired infections (HAIs). In addition to the clinical burden of infection, the growing number of MRSA-colonized patients has profound implications for patient outcomes and resource utilization, which have yet to be adequately studied and addressed. MRSA-colonized patients, who are placed on contact precautions when admitted, may experience longer waiting times for hospital bed assignment and decreased interactions with providers, more preventable adverse events, as well as increased risks of inappropriate antibiotic use and dissatisfaction with care. Clinicians and policymakers may consider several strategies to address this problem, each of which has clinical and resource trade-offs. The value of applying mathematical modeling techniques, and specifically that of discrete event simulation (DES), to infection control research, has yet to be fully realized. This K01 application proposes three hypothesis-driven aims to advance the fields of infection control, hospital epidemiology and antimicrobial resistance: 1) to estimate the relationship between colonization history and antimicrobial prescribing, time-to-bed-assignment and within-hospital patient transfers, using a novel patient data warehouse; 2) to design and validate a DES model of patient flow in a tertiary care hospital; and 3) to apply the validated DES model to estimate the clinical and resource utilization outcomes of alternative infection control strategies. This innovative and multi-disciplinary study will provide clinicians and policymakers with valuable information through the quantification of the trade-offs of competing screening approaches. I am a physician at the Massachusetts General Hospital and an Instructor in Medicine at Harvard Medical School trained in infectious diseases with a doctorate in health policy/economics. I have designed and implemented two clinical research studies in the field of MRSA surveillance and discontinuation of contact precautions as well as two national surveys on the impact of contact precautions. I will use the K award to expand my current skill set to include analysis and management of large databases, mathematical modeling (specifically, discrete event simulation) and optimization methods. I will be mentored by Dr. David Hooper, an infectious disease clinician and expert in the fields of infection control and antimicrobial resistance, and Dr. Rochelle Walensky, an infectious disease clinician and mathematical modeler. I will collaborate with experts in the field of biostatistics, operations research and information systems. The targeted educational curriculum, mentoring plan and research strategy will ensure my successful transition over the period of the Award to an Independent Investigator with expertise in the use of modeling techniques to evaluate infection control approaches and implement optimal strategies in the clinical setting. I will use the valuable information gleaned from modeling to design rigorous research studies to advance the fields of infection control, hospital epidemiology and antimicrobial resistance.