2457. Data Science for Outbreak Investigation: Identifying Risk Factors, Tracing Contacts, and Eliciting Transmission Pathways in a Vancomycin-Resistant Enterococci (VRE) Outbreak

Abstract

BackgroundIn 2018 we experienced a nosocomial outbreak due to vancomycin-resistant enterococci (VRE) in our hospital network. Our goals were to characterize risk factors for VRE acquisition, elicit potential hot spots of transmission, and delineate an optimized approach to tracing contacts.MethodsWe assembled diverse datasets of variable quality and covering different aspects of care from electronic medical records generated during the outbreak period (1/2018–9/2018). Patients who tested VRE-positive during this period were compared with controls with up to 3 negative screenings. First, we identified risk factors for VRE colonization by means of uni- and multivariate analyses. Next, we elicited transmission pathways by detecting commonalities between VRE cases, and determined whether patients with characteristics and connections similar to VRE cases were missed by our current contact tracing strategy.ResultsWe compared 221 VRE patients to 33,624 controls. Independent predictors of VRE colonization were ICU admission (OR 4.9, with 95% confidence interval [3.7–6.5], P < 0.001)], number of records in the database (a proxy for severity-of-illness, OR 1.1 [1.1–1.1], P < 0.001), length of hospital stay (OR 2.7 [2.0–3.5], P < 0.001), age (OR 1.3 [1.2–1.4], P < 0.001), and weeks of antibiotics (OR 1.2 [1.1–1.3], P < 0.001). By using complex network analysis, we were able to establish three main pathways by which the 221 VRE cases are connected: healthcare personnel, medical devices, and patient rooms. This multi-dimensional network extends beyond our current contact tracing strategy, which captures inpatients based on geographical proximity (cf. figure).ConclusionIn this outbreak investigation based on a large electronic healthcare data collection, we found three main risk factors for being a VRE carrier (ICU admission, length of hospital stay, antibiotic exposure), along with three important links between VRE cases (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations should take data maturity, the scope of included sources, and potential confounding factors into account. Disclosures All authors: No reported disclosures.