1019. Defining electronic patient phenotypes to inform risk-adjustment strategies in hospital antimicrobial use comparisons

Abstract

Abstract Background Comparison of antimicrobial use (AU) rates among hospitals can identify areas to intervene for antimicrobial stewardship. Hospital AU interpretation is difficult without risk-adjustment for patient mix. Identifying high- or low-risk patient characteristics, or “electronic phenotypes,” for receipt of antimicrobials using data from electronic health records (EHR) could help define risk-adjustment factors AU comparisons. Methods We performed a retrospective study of EHR-derived data from adult and pediatric inpatients within the Duke University Health System from October 2015 to September 2017. Encounters were included if the patient spent time in an inpatient location. The analysis aimed to identify subpopulations that were high- or low-risk for antimicrobial exposure based on EHR data summarized on the encounter level. Antimicrobial days of therapy (DOT) and days present, representing the length of stay (LOS), were defined as in the 2018 NHSN AU Option. Location exposures were defined in binary variables if patients were housed at least 1 day on a hospital unit type. We compared antimicrobial-exposed to unexposed patients as well as DOT among various factors including demographics, location, nonantimicrobial medications, labs, ICD-10 codes, and diagnosis-related groups (DRG). Results The EHR-derived dataset included 170,294 encounters and 204 variables in one academic and two community hospitals; 80,192 (47%) received at least one antimicrobial. Distributions of both LOS and DOT were zero-inflated and skewed by long outliers (figure). Encounters with >=7 DOT made up 63% of total DOT, but only 9% of inpatient encounters. Electronic phenotypes with highest DOT included those with long lengths of stay, older age, exposures to stem cell transplant, pulmonary, and critical care units, and DRG that included transplant, respiratory, or infectious diagnoses. Zero DOT phenotypes included those with short lengths of stay, exposure to labor and delivery wards, medical wards, and DRG that included birth and pregnancy. Conclusion Future work in defining risk-adjustment factors for hospital AU data comparisons should determine if factors associated with low- or high-risk electronic phenotypes assist in prediction of antibiotic use. Disclosures All authors: No reported disclosures.