Program Schedule

943
Prospective evaluation of a cluster of Pseudomonas aeruginosa isolates identified by automated statistical software

Session: Poster Abstract Session: Outbreaks
Friday, October 10, 2014
Room: The Pennsylvania Convention Center: IDExpo Hall BC
Posters
  • IDWeek_Cloonan.pdf (532.5 kB)
  • Background:

    Traditionally, nosocomial outbreaks are identified by reports from healthcare workers or review of microbiologic data. While these methods uncover the most egregious instances of nosocomial transmission, they are ultimately crude and insensitive. To improve upon the current state of outbreak detection we implemented a system which combines automated surveillance, epidemiological investigation and strain typing. In the present work, we report our initial experience with prospective assessment of a cluster of an important nosocomial pathogen identified by automated surveillance.

    Methods:

    The WHONET-SaTScan cluster detection tool was used to identify clusters of Pseudomonas aeruginosa in clinical cultures between 1/2013-2/2014. Simulated, prospective surveillance using the space-time permutation model was used to detect temporal clusters of unique isolates by patient on the same hospital unit with a maximum cluster length of 60 days. A cut-off value of p= 0.05 was used to identify clusters compared to a 1-year baseline incidence. Two evaluators independently assessed each cluster by analyzing records of patient movements and the antibiogram of the isolates. Isolates from a plausible cluster were analyzed by multilocus sequence typing (MLST).

    Results:

    Epidemiological investigation indicated that nosocomial transmission was unlikely in 20 of 21 clusters identified by automated statistical surveillance. One epidemiologically plausible cluster of three isolates was not identified by routine surveillance. Genotyping of this cluster and control isolates from the same unit and a second unit showed a different sequence type for each isolate tested.

    Conclusion:

    Prospective use of automated statistical surveillance identified clusters of potential transmission missed by standard approaches, and the application of investigative tools including strain typing ruled out recent transmission in an otherwise plausible cluster. This methodology is practical and may allow focused use of infection control measures targeted at interrupting transmission.

    Sean Cloonan, MD1, Anna Stachel, CIC2, Kristina Ernst3, Kenneth Inglima, MS4, Catharine Prussing, MHS5, Bo Shopsin, MD, PhD1, Hannah Rose3, Donald Chen, MD2, Jennifer Lighter, MD2, Maria Aguero-Rosenfeld, MD4 and Michael Phillips, MD2, (1)Department of Medicine, Division of Infectious Diseases, NYU School of Medicine, New York, NY, (2)Infection Prevention and Control, NYU Langone Medical Center, New York, NY, (3)NYU School of Medicine, New York, NY, (4)Clinical Microbiology, NYU Langone Medical Center, New York, NY, (5)Bureau of Communicable Disease, NYC Department of Health and Mental Hygiene, Long Island City, NY

    Disclosures:

    S. Cloonan, None

    A. Stachel, None

    K. Ernst, None

    K. Inglima, None

    C. Prussing, None

    B. Shopsin, None

    H. Rose, None

    D. Chen, None

    J. Lighter, None

    M. Aguero-Rosenfeld, None

    M. Phillips, None

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