Analyzing Relationships Between Contracting a HAI and Patient Epidemiology

Overview

Hospital Acquired Infections (HAI) are of growing concern in the healthcare market because they lead to a 6% mortality rate, increase the average length of stay for a patient from five to twenty-two days, and cost the healthcare industry a projected $8.8 billion per year. In the analysis of hospital acquired infections, identification of specific risk factor families, combinations of variables in patient care such as length of stay and age, that lead to the contraction of a hospital acquired infection have not been clearly defined. This study focuses on eight hospital acquired infections of interest: Ventilator Associated Pneumonia, MRSA Septicemia, MSSA Septicemia, Pseudamonas aeruginosaSepticemia, Clostridium difficile Infection, Escherichia coli Septicemia, Legionnaires’ Disease, and Gastroenteritis and evaluates the relationships between the risk factor families within each HAI to provide better insight into the factors that lead to the contraction of a HAI, which is then used to build a predictive model to provide the susceptibility rating for a patient based on epidemiological data available in their electronic health record. The identification of risk factor families for the data set of 174,804 encounter patients with 126 associated variables accounts for the complex influence of variables in the development of a hospital acquired infection in a patient, and the predictive model trained on this data is the first to provide a susceptibility rating for the contraction of a HAI of interest based on the highly accessible information available in a patient’s EHR. This model could be implemented to better inform physicians of the susceptibility of their patients to contracting a HAI, which could lead to a more informed decision as to the quantity and type of antibiotics to administer to patients upon admission or during their course of treatment in the hospital.

Competition history

• AJAS 2019 Category not listed

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