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Integration of genomic and clinical data augments surveillance of healthcare-acquired infections

Published online by Cambridge University Press:  23 April 2019

Doyle V. Ward Open the ORCID record for Doyle V. Ward [Opens in a new window] ,
Andrew G. Hoss ,
Raivo Kolde ,
Helen C. van Aggelen ,
Joshua Loving ,
Stephen A. Smith ,
Deborah A. Mack ,
Raja Kathirvel ,
Jeffery A. Halperin  and
Douglas J. Buell
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Doyle V. Ward*
Affiliation:
Center for Microbiome Research, University of Massachusetts Medical School, Worcester, Massachusetts Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts
Andrew G. Hoss
Affiliation:
Philips Research North America (PRNA), Cambridge, Massachusetts
Raivo Kolde
Affiliation:
Philips Research North America (PRNA), Cambridge, Massachusetts
Helen C. van Aggelen
Affiliation:
Philips Research North America (PRNA), Cambridge, Massachusetts
Joshua Loving
Affiliation:
Philips Research North America (PRNA), Cambridge, Massachusetts
Stephen A. Smith
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Deborah A. Mack
Affiliation:
Infection Control Department, UMass Memorial Medical Center, Worcester, Massachusetts
Raja Kathirvel
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Jeffery A. Halperin
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Douglas J. Buell
Affiliation:
IT-Data Sciences and Technology, University of Massachusetts Medical School, Worcester, Massachusetts
Brian E. Wong
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Judy L. Ashworth
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Mary M. Fortunato-Habib
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Liyi Xu
Affiliation:
Philips Research North America (PRNA), Cambridge, Massachusetts
Bruce A. Barton
Affiliation:
Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts
Peter Lazar
Affiliation:
Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts
Juan J. Carmona
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Jomol Mathew
Affiliation:
Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts
Ivan S. Salgo
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Brian D. Gross
Affiliation:
Genomics for Infectious Disease (G4ID) Unit, Patient Care Analytics, Philips Healthcare, Cambridge, Massachusetts
Richard T. Ellison III
Affiliation:
Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts
*
Author for correspondence: Doyle V. Ward, Email: [email protected]
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Abstract

Background:

Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.

Objective:

To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.

Methods:

Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.

Results:

Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.

Conclusions:

Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 6 , June 2019 , pp. 649 - 655
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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