1365. An evaluation of an automated hospital outbreak detection system (WHONET-SaTScan) versus standard outbreak detection approach
Session: Poster Abstract Session: HAI: Epidemiologic Methods
Friday, October 28, 2016
Room: Poster Hall
Posters
  • IDWeek_SatScanPoster_AStachel.pdf (287.5 kB)
  • Background:

    A recent survey on current hospital outbreak detection systems (ODS) show hospitals rely on a manual review of empirical rules of no more than 9 pre-identified organisms to identify potential outbreaks.1 We use a novel software package, WHONET-SaTScan (WS), to facilitate an automated outbreak detection system via space/time to improve standard outbreak detection approach (SODA).

    Methods:

    We established WS along with a database to detect, store, link electronic health record (EHR) data and record results from investigations/interventions of clusters to create an automated ODS.  We conducted surveillance for 490 bacterial/fungal organisms along with antibiotic resistance phenotypes for 11 organisms.  For patients identified as part of a cluster, the following information was extracted from the EHR: possible environmental organism; shared unit, room, medical providers and antibiotic susceptibility patterns.  These data were used to stratify clusters into 2 risk groups (high vs low). Using the CDC’s guidelines for evaluating a surveillance system, we assessed system attributes (i.e. simplicity, flexibility, sensitivity, timeliness and usefulness).2 

    Results:

    During an 8 month time period (Sep 1, 2015 – Apr 30, 2016), 146 clusters were detected, 39 of which were considered high risk clusters.  Of these 39 clusters, 16 organisms were identified, with S. aureus (21%), S. maltophilia (15%) and K. peumoniae (13%) representing the majority (Fig 1).  Three transmission events were determined to have likely occurred based on demographic factors and review of disinfection practices.  Only 1 of these transmission events was identified through an astute clinician.  WS did not miss any clusters identified by SODA. The median number of days for WS to detect a cluster was 16.

    Conclusion:

    WS is a relatively simple ODS that offers flexibility in creating the parameters (e.g. outbreak window, significance threshold, baseline data) and can identify clusters in space/time.  The output is simple to navigate; however, the process to implement software and link with EHR data required additional IT skills. The system has high sensitivity, albeit low specificity. The median time to detection was 16 days; however, many clusters were detected within 3 days.  WS is a useful addition to a robust infection control program.

     

     

    Anna Stachel, MPH, CIC1, Gabriela Pinto, BA1, John Stelling, MD, MPH2, Bo Shopsin, MD, PhD3, Kenneth Inglima, MS4 and Michael Phillips, MD1, (1)Infection Prevention and Control, NYU Langone Medical Center, New York, NY, (2)Brigham and Women's Hospita, Boston, MA, (3)Department of Medicine, Division of Infectious Diseases, NYU School of Medicine, New York, NY, (4)Clinical Microbiology, NYU Langone Medical Center, New York, NY

    Disclosures:

    A. Stachel, None

    G. Pinto, None

    J. Stelling, None

    B. Shopsin, None

    K. Inglima, None

    M. Phillips, None

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