Abstract Background Carbaepenem-resistant Enterbacteriaceae (CRE) could propagate through person-to-person contacts such as contaminated hands of staff, contamination of the environment, or the use of contaminated medical equipment by spreading carbapenemase encoded in plasmid and cause healthcare associated infections (HAIs). Critically ill patients in a compact space of Intensive care unit (ICU) are at significantly high risk of CRE infection, which should be protected by effective infection control strategies. Agent-based model (ABM) has been employed to examine the spread of infectious organisms and estimate the impact of intervention as it reflects behaviors of heterogeneous individuals. We aimed to create an ABM reflecting the medical ICU and identify preventative strategies to reduce CRE infection in the ICU. Methods The blueprint of the medical ICU in a tertiary hospital in Republic of Korea was obtained. Researchers had a meeting with the head staffs to get information about healthcare workers (HCWs), patients, and the spatial unit. In this unit, HCWs visiting a patient in ICU are required to wash their hands and wear personal protective equipment. We did a survey to collect data of the routes and the handwashing rates of HCWs. An ABM was made using Python’s Mesa library which simulates the situation in the ICU and the status of CRE. Figure 1.(A) Blueprint of ICU in Severance Hospital, (B) Simulated ICU in agent-based modelFigure 2.Dynamics between agents and environment Results Medical ICU consists of two units, and each unit holds fifteen beds. Five nurses work in each unit and six doctors and three technicians take rounds daily, contacting patients in the ICU. The dynamics among agents and environment were constructed.. Parameters were set up from the real world data or the clinical studies. At any given moment, there are about 2.5 patients infected with CRE, and transmission probability and isolation factors were calibrated according to this target. Interventions were enhanced environment cleansing, handwashing of HCWs, and prompt isolation of infected patients through tests and actions. We found those interventions effectively reduced HAIs, and combining effects lead to better results. Figure 3.Parameters and variables in agend-based modelFigure 4.Healthcare associated infections in study periods according to (A) interval of clensing environment, (B) handwashing of HCWs, (C) isolation of infected patients, (D) combined of environmental cleansing and patient isolation Conclusion Increasing handwashing rates, environment cleansing, and prompt isolation of infected patients have drastic effects of reducing HAIs caused by CRE in this model, and bundle approach should be considered to prevent the spread of CRE. Disclosures All Authors: No reported disclosures.
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