1248. Genomic Sequencing and Clinical Data Integration for Next-Generation Infection Prevention

Abstract

BackgroundTypical Infection Prevention to detect pathogen transmission in hospitals has relied on observation of (1) uncommon pathogen phenotypes or (2) greater than expected number of pathogen phenotypes in a given timeframe and/or location. Genome sequencing of targeted organisms in conjunction with routine patient geo-temporal information and antibiotic susceptibility data holds promise in identifying transmissions with greater sensitivity and specificity, saving time and effort in reviewing for transmission events.MethodsIn an on-going genomic sequencing surveillance effort in a tertiary care hospital, drug-resistant clinical isolates from the “ESKAPE” pathogens were routinely sequenced in 2017. In parallel, potential clusters were identified for 2017 through conventional Infection Prevention approaches. Groups identified by their genetic distances along with visualizations on antimicrobial susceptibilities, and patient location histories and dates were displayed in an interactive interface, Philips IntelliSpace Epidemiology (PIE), and reviewed by Infection Prevention.ResultsAmong 656 patients, 1,239 drug-resistant ESKAPE samples were sequenced. Thirty-eight genetically related groups involving 196 patients were identified. Groups ranged in size from two to 44 patients, primarily consisting of VRE and MRSA. Notably, a review of the 38 groups identified 20 groups where the information at hand suggested a concern for transmission. 16 of the 20 were not previously identified by Infection Prevention. Using PIE to review all 38 groups identified from 1 year’s worth of data required 3 hours of time by an Infection Prevention professional, averaging less than 5 minutes per cluster, less than 1 minute per patient, and 11 minutes of review time per actionable opportunity. By conventional means, approximately 23 hours would have been required to review the genomic groups without the aid of the PIE tool.ConclusionThe use of PIE’s genomic-defined groups, along with the integrated clinical data platform, allows for a greater ability, certainty, and speed to detect clusters of organisms representing transmission in the hospital setting. Applied prospectively, PIE can detect transmissions sooner than by conventional means for potential patient safety gains and cost savings.Disclosures D. Chen, Philips: Scientific Advisor, Consulting fee. M. Fortunato-Habib, Philips Healthcare: Collaborator and Employee, Salary. A. Hoss, Philips: Employee, Salary. R. Kolde, Philips: Employee, Salary. A. Dhand, Merck: Speaker’s Bureau, Speaker honorarium. Astellas: Scientific Advisor, Consulting fee. R. Sussner, Philips: Scientific Advisor, Consulting fee. J. Carmona, Philips Healthcare: Employee, Salary. B. Gross, Philips Healthcare: Employee, Investigator, Research Contractor, Scientific Advisor and Shareholder, Salary. J. Fallon, Philips Healthcare: Investigator, Research support.

Integration of genomic and clinical data augments surveillance of healthcare-acquired infections

“…Thus, our cloud-computing approach has potential to inform infection control practice proactively. Using the PIE cloudcomputing environment, we were able to analyze and generate potential relatedness matches for the entire dataset in~3 hours, which is consistent with another estimate by Chen et al, 30 who also used the platform. Thus, we were able to achieve a time scale that is relevant for more rapid clinical investigation and intervention to identify and limit outbreaks, which contributes to improving overall patient outcomes.…”