1248. Genomic sequencing and clinical data integration for next-generation Infection Prevention
Session: Poster Abstract Session: Healthcare Epidemiology: Outbreaks
Friday, October 5, 2018
Room: S Poster Hall
  • IDweek_poster_1002.pdf (1.9 MB)
  • Background:

    Typical 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.


    In 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.


    Among 656 patients, 1239 drug-resistant ESKAPE samples were sequenced. 38 genetically related groups involving 196 patients were identified. Groups ranged in size from 2 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 one year’s worth of data required three hours of time by an Infection Prevention professional, averaging less than five minutes per cluster, less than one 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.


    The 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.

    Donald Chen, MD1, Liyi Xu, PhD2, Mary Fortunato-Habib, DNP, MS, RN3, Andrew Hoss, PhD2, Melissa Chanza, BA4, Changhong Yin, MD4, Raivo Kolde, PhD2, Abhay Dhand, MD5, Rita Sussner, BSN, RN, CIC1, Juan Carmona, PhD, MPH, MBE3, Guiqing Wang, MD/PhD6, Weihua Huang, PhD4, Brian Gross, M.Sc., BSEE, RRT, SMIEEE3 and John Fallon, MD4, (1)Infection Prevention and Control, Westchester Medical Center, Valhalla, NY, (2)Philips Research North America, Cambridge, MA, (3)Philips Innovation Lab, Philips Healthcare, Cambridge, MA, (4)Pathology, New York Medical College, Valhalla, NY, (5)Medicine, Westchester Medical Center/New York Medical College, Valhalla, NY, (6)Pathology and Clinical Laboratories, Westchester Medical Center, Valhalla, NY


    D. Chen, Philips: Scientific Advisor , Consulting fee .

    L. Xu, None

    M. Fortunato-Habib, Philips Healthcare: Collaborator and Employee , Salary .

    A. Hoss, Philips: Employee , Salary .

    M. Chanza, None

    C. Yin, None

    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 .

    G. Wang, None

    W. Huang, None

    B. Gross, Philips Healthcare: Employee , Investigator , Research Contractor , Scientific Advisor and Shareholder , Salary .

    J. Fallon, Philips Healthcare: Investigator , Research support .

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