842. Analysis of inpatient Clostridium difficile (CD) transmission by traditional genotyping and whole genome phylogeny.
Session: Oral Abstract Session: Clostridium difficile
Thursday, October 27, 2016: 3:00 PM
Room: 388-390

Background:  Due to persistently high Clostridium difficile infection (CDI) rates on 2 units housing immunocompromised patients whole genome sequencing (WGS) of CD isolates was performed to enhance our understanding of transmission dynamics.

Methods: We combined spatio-temporal analysis with retrospective WGS on CDI isolates from 2013 to construct a CDI transmission network.   WGS and phylogenetic analyses were performed on 23 samples (done in 4x replicates).  We calculated multilocus sequence types (MLST) from the WGS data for comparison with spatio-temporal analysis.

Results:  Spatial and temporal overlap of patients suggested 6 possible CDI transmission events among patients who occupied the same wards contemporaneously and 2 possible instances of transmission based on ward contamination in patients with consecutive admissions (Table 1). Four predominant MLSTs (15 of the 23 isolates) were grouped geospatially (Figure 1).  MLST characterization identified 2 (T+/G+) direct transmission events, 1 ward contamination event and 5 identical strain types without hospital overlap.  WGS SNP analysis identified 4 distinct groups of related strains (Figure 2). Two clusters likely involved transmission via ward contamination and two patients with identical strains by WGS had no apparent hospital contact.  The largest cluster, involving 5 highly related isolates included 1 T+/G and 4 T-/G-, pairs suggesting both direct and environmental/non-ward based transmission. 

Conclusion: Spatial and temporal analysis can suggests cross transmission that is not substantiated by WGS, while WGS and phylogenetic analysis of CDI cases can enhance identification of routes of transmission without obvious co-location. WGS may reveal previously unappreciated modes of cross contamination that need to be addressed within the hospital environment to reduce the overall incidence of CDI. More complete populations sampling is need to fully elucidate transmission dynamics among hospitalized patients

Table 1: Temporal, Geospatial and MLST Overlap

Isolate Pair

Temporal (T)

Geospatial (G)

MLST

269/378

+

+

33/69

393/403

+

+

33/33

355/461

+

+

19/3

283/287

+

+

19/19

490/509

+

+

19/3

568/637

+

+

33/19

568/663

-

+

33/33

637/663

-

+

19/3

Figure1.  Temporal-spatial map of CDI cases.

Figure 2. Phylogenetic tree of CDI isolates.

Elizabeth Robilotti, MD MPH1, Nitin Kumar, PhD2, Niaz Banaei, MD3, Trevor Lawley, PhD2 and Lucy Tompkins, MD, PhD, FSHEA, FIDSA4, (1)Infectious Diseases/Infection Control, Memorial Sloan Kettering Cancer Center, New York, NY, (2)Wellcome Trust Sanger Institute, Cambridge, United Kingdom, (3)Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, (4)Infection Control and Prevention, Stanford Health Care, Stanford, CA

Disclosures:

E. Robilotti, None

N. Kumar, None

N. Banaei, Karius Inc: Investigator , Research support

T. Lawley, None

L. Tompkins, None

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