In silico Randomized Control Trial Framework for Assessing Infection Control and Prevention Interventions in the Hospital

项目摘要/摘要 多药耐药菌(MDRO)，特别是碳青霉烯耐药菌(CRO)， 是医疗保健相关感染(HAI)的主要原因。尽管研究发现 解决MDRO定植和传播的多种干预措施在以下方面有效 减少HAI，由于不确定最有效的方案，实施工作一直滞后 干预措施的组合。随着患者在医疗机构内通过 医护人员(HCW)和移动设备的移动，干预措施的评估 对传播和感染的影响必须考虑这些网络动态。此外，每个 机构有自己的人员配置比例、设备管理和清洁做法， 告知干预措施的效果。解决关于最有效的 结合个别医院的干预策略，我们将建立一个模型 将为医院管理员和感染控制专家提供工具的框架 量化单个患者的定植和感染风险因素，并比较 控制禽流感的干预措施的潜在有效性。这项计划的目标是：(1) 建立模型来预测哪些患者有最高的定植和感染风险 MDRO使用在正常护理过程中收集的临床相关信息 结合人员编制和设备移动的业务数据；(2)利用医院- 水平模型，以量化之间的组合关系和边缘影响， 额外的干预措施，以减少禽流感；及(3)为其他医院和 医疗保健系统检查其自身参数化的干预措施的有效性 机构的数据。模型将以患者为中心，利用丰富的协变量数据 存储在电子健康档案中，以开发基于先进机器的预测模型 学习。这些预测模型将是可翻译的工具，可以直接合并 进入临床护理，协助临床医生预防禽流感。有关患者连接性的数据将 形成医院级模型的基础，允许对 不同干预措施组合的有效性。最后，一个可概括的框架将 在医疗设施网络中进行建造和测试。这些型号将直接 告知疾控中心有关MDRO预防的指南，并帮助临床医生降低HAIS的风险。

Project Summary/Abstract Multidrug-resistant organisms (MDROs), particularly carbapenem-resistant organisms (CROs), are a major cause of healthcare-associated infections (HAIs). Though studies have found multiple interventions addressing MDRO colonization and transmission to be effective at reducing HAIs, implementation has lagged due to uncertainty regarding the most efficacious combinations of interventions. As patients are connected within healthcare facilities by the movement of healthcare workers (HCWs) and mobile equipment, evaluation of an intervention's impact on transmission and infection must consider these network dynamics. Additionally, each institution has its own staffing ratios, equipment management, and cleaning practices that inform the efficacy of an intervention. To address knowledge gaps regarding the most effective combinations of intervention strategies at an individual hospital, we will build a model framework that will provide hospital administrators and infection control experts with tools for quantifying individual patient risk factors for colonization and infection and for comparing the potential effectiveness of interventions to control HAIs. The aims of the project are: (1) to develop models to predict which patients are at highest risk for colonization and infection with MDROs using clinically relevant information collected during the normal course of care combined with operational data on staffing and equipment movement; (2) to utilize hospital- level models to quantify the combinatorial relationship between, and marginal impact of, additional interventions to reduce HAIs; and (3) to build a framework for other hospitals and healthcare systems to examine the effectiveness of interventions parameterized by their own institution's data. Models will start with the patient at the center, utilizing the rich covariate data stored in electronic health records to develop prediction models based on advanced machine learning. These prediction models will be translatable tools that can be directly incorporated into clinical care to assist clinicians in preventing HAIs. Data on patient-connectedness will form the foundation of hospital-level models that will allow for detailed examinations of the effectiveness of different combinations of interventions. Finally, a generalizable framework will be constructed and tested across a network of healthcare facilities. These models will directly inform CDC guidelines about MDRO prevention and aid clinicians in reducing the risk of HAIs.