Improving clinical, operational and economic outcomes related to MRSA

描述(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(HAI)的原因。除了感染的临床负担外，越来越多的MRSA定植患者对患者结局和资源利用具有深远的影响，这些问题尚未得到充分的研究和解决。被MRSA定植的患者在入院时需要采取接触预防措施，他们可能会经历更长的医院床位分配等待时间，与提供者的互动减少，更多可预防的不良事件，以及不适当使用抗生素和对护理不满的风险增加。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，每一种策略都有临床和资源方面的权衡。应用数学建模技术，特别是离散事件仿真(DES)技术在传染病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，以促进感染控制、医院流行病学和抗菌素耐药性领域的发展：1)使用新的患者数据仓库，估计定殖史与抗菌药物处方、到床时间分配和医院内患者转移之间的关系；2)设计和验证三级护理医院的患者流动的DES模型；以及3)应用经验证的DES模型来估计替代感染控制策略的临床和资源利用结果。这项创新的多学科研究将通过量化相互竞争的筛查方法的权衡，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内科医生，哈佛医学院的医学讲师，受过传染病方面的培训，拥有卫生政策/经济学博士学位。我在耐甲氧西林金黄色葡萄球菌监测和停止接触预防措施领域设计并实施了两项临床研究，以及关于接触预防措施影响的两项全国性调查。我将利用K奖来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。我将得到传染病临床医生和感染控制和抗菌素耐药性领域的专家大卫·胡珀博士的指导，以及传染病临床医生和数学建模专家罗谢尔·瓦伦斯基博士的指导。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、指导计划和研究战略将确保我在获奖期间成功过渡到一名独立调查员，在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方面具有专业知识。我将使用从建模中收集的有价值的信息来设计严格的研究研究，以推进感染控制、医院流行病学和抗菌素耐药性领域的发展。

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.