947. Visualization of inpatient C. difficile transmission events: Can pictures speak louder than words?
Session: Oral Abstract Session: HAI: Surveillance and Reporting
Friday, October 28, 2016: 11:45 AM
Room: 388-390
Background: Electronic surveillance applications are helpful tools for infection preventionists. We used a novel visual approach to evaluate for hospital level geographical clustering of C. difficile infection (CDI), and to characterize the geographical relationship of C. difficile "pressure" and subsequent a) hospital-acquired (HA)-CDI infections and b) healthcare-associated (HCA)-CDI.

Methods: We calculated CDI prevalence using a small CDI dataset from 5 Duke University Hospital wards with the highest CDI prevalence during phase A (standard terminal clean) and phase B (standard terminal clean + UV light) of the Benefits of Enhanced Terminal Room Disinfection (BETR-D) study to create 3 maps: 1) CDI pressure (hotspots of CDI), 2) HA-CDI, and 3) HCA-CDI. Our general prevalence formula was: (number of CDI days in room X/total number of occupied days in room X). We calculated the numerator for each map as follows: 1) CDI pressure included all days on or after which a patient was diagnosed with CDI a) while admitted or b) 30 days prior to admission into the room; 2) HA-CDI included days on or after a patient was diagnosed with HA-CDI, defined as CDI with positive test >48 hours after admission; and 3) HCA-CDI included admission days that a patient was diagnosed with a) HA-CDI or b) with CDI diagnosed within two weeks after admission. Table 1 lists overall prevalence for each map during each phase of the study. Prevalence quartiles were calculated after removing zeros.

Results: CDI pressure was similar between the two study phases (table 1). Figure 1 displays the cancer ward prevalence maps for CDI pressure, HA-CDI, and HCA-CDI. Hot spots of HA- and HCA-CDI were located in the same physical areas of CDI pressure hot spots

Conclusion: Visual analytics are a novel way to demonstrate environmental hotspots. Future standardized programs will enable infection preventionists to visualize clustering of infections, prompting earlier outbreak investigations and focused enhanced cleaning efforts.

Bronwen Garner, MD MPH1, Sarah S. Lewis, MD MPH2, Rebekah W. Moehring, MD, MPH2, Daniel Sexton, MD3, Deverick Anderson, MD, MPH, FIDSA, FSHEA2, Penny Cooper, DHSc4, William Rutala, PhD, MPH, FSHEA5, David J. Weber, MD, MPH, FIDSA, FSHEA6 and Duke University CDC Prevention Epicenter Program, (1)Infectious Disease, Duke University Medical Center, Durham, NC, (2)Division of Infectious Diseases, Duke University Medical Center, Durham, NC, (3)Duke Antimicrobial Stewardship Outreach Network, Durham, NC, (4)Augusta Health, Fishersville, VA, (5)Duke University CDC Prevention Epicenter Program, Durham, NC, (6)Medicine, Pediatrics, Epidemiology, University of North Carolina, School of Public Health, Chapel Hill, NC

Disclosures:

B. Garner, None

S. S. Lewis, None

R. W. Moehring, None

D. Sexton, None

D. Anderson, None

P. Cooper, None

W. Rutala, None

D. J. Weber, None

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