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

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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.