Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-24T11:27:45.528Z Has data issue: false hasContentIssue false
  • English
  • Français

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
...Show all authors
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: doyle.ward@umassmed.edu
Get access Rights & Permissions [Opens in a new window]

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. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, DJ, Podgorny, K, Berrios-Torres, SI, et al. Strategies to prevent surgical site infections in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol 2014;35:605–627.CrossRefGoogle ScholarPubMed
Lo, E, Nicolle, LE, Coffin, SE, et al. Strategies to prevent catheter-associated urinary tract infections in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol 2014;35:464–479.CrossRefGoogle ScholarPubMed
Marschall, J, Mermel, LA, Fakih, M, et al. Strategies to prevent central line-associated bloodstream infections in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol 2014;35:753–771.CrossRefGoogle ScholarPubMed
Anderson, DJ, Chen, LF, Weber, DJ, et al. Enhanced terminal room disinfection and acquisition and infection caused by multidrug-resistant organisms and Clostridium difficile (the Benefits of Enhanced Terminal Room Disinfection Study): a cluster-randomised, multicentre, crossover study. Lancet 2017;389:805–814.CrossRefGoogle ScholarPubMed
Calfee, DP, Salgado, CD, Milstone, AM, et al. Strategies to prevent methicillin-resistant Staphylococcus aureus transmission and infection in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol 2014;35:772–796.CrossRefGoogle ScholarPubMed
Dubberke, ER, Carling, P, Carrico, R, et al. Strategies to prevent Clostridium difficile infections in acute care hospitals: 2014 Update. Infect Control Hosp Epidemiol 2014;35:628–645.CrossRefGoogle ScholarPubMed
Ellingson, K, Haas, JP, Aiello, AE, et al. Strategies to prevent healthcare-associated infections through hand hygiene. Infect Control Hosp Epidemiol 2014;35:937–960.CrossRefGoogle ScholarPubMed
Fenoll, A, Jado, I, Vicioso, D, Casal, J. Dot blot assay for the serotyping of pneumococci. J Clin Microbiol 1997;35:764–766.Google ScholarPubMed
Sorensen, UB. Typing of pneumococci by using 12 pooled antisera. J Clin Microbiol 1993;31:2097–2100.Google ScholarPubMed
Perez-Losada, M, Cabezas, P, Castro-Nallar, E, Crandall, KA. Pathogen typing in the genomics era: MLST and the future of molecular epidemiology. Infect Genet Evol 2013;16:38–53.CrossRefGoogle ScholarPubMed
Goering, RV. Pulsed field gel electrophoresis: a review of application and interpretation in the molecular epidemiology of infectious disease. Infect Genet Evol 2010;10:866–875.CrossRefGoogle ScholarPubMed
Gilchrist, CA, Turner, SD, Riley, MF, Petri, WA Jr, Hewlett, EL. Whole-genome sequencing in outbreak analysis. Clin Microbiol Rev 2015;28:541–563.CrossRefGoogle ScholarPubMed
Quainoo, S, Coolen, JPM, van Hijum, S, et al. Whole-genome sequencing of bacterial pathogens: the future of nosocomial outbreak analysis. Clin Microbiol Rev 2017;30:1015–1063.CrossRefGoogle ScholarPubMed
Aanensen, DM, Feil, EJ, Holden, MT, et al. Whole-genome sequencing for routine pathogen surveillance in public health: a population snapshot of invasive Staphylococcus aureus in Europe. mBio 2016;7(3):e00444–16.CrossRefGoogle Scholar
Azarian, T, Cook, RL, Johnson, JA, et al. Whole-genome sequencing for outbreak investigations of methicillin-resistant Staphylococcus aureus in the neonatal intensive care unit: time for routine practice? Infect Control Hosp Epidemiol 2015;36:777–785.CrossRefGoogle ScholarPubMed
Gorrie, CL, Mirceta, M, Wick, RR, et al. Antimicrobial resistant Klebsiella pneumoniae carriage and infection in specialized geriatric care wards linked to acquisition in the referring hospital. Clin Infect Dis 2018;67:161–170.CrossRefGoogle ScholarPubMed
Quick, J, Cumley, N, Wearn, CM, et al. Seeking the source of Pseudomonas aeruginosa infections in a recently opened hospital: an observational study using whole-genome sequencing. BMJ Open 2014;4(11):e006278.CrossRefGoogle Scholar
Snitkin, ES, Zelazny, AM, Thomas, PJ, et al. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci Transl Med 2012;4(148):148ra16.CrossRefGoogle ScholarPubMed
National Healthcare Safety Network (NHSN). Patient Safety Component Manual. Atlanta, GA: Centers for Disease Control and Prevention; 2019.Google Scholar
Shorr, AF, Tabak, YP, Killian, AD, Gupta, V, Liu, LZ, Kollef, MH. Healthcare-associated bloodstream infection: A distinct entity? Insights from a large US database. Crit Care Med 2006;34:2588–2595.CrossRefGoogle Scholar
Storr, J, Twyman, A, Zingg, W, et al. Core components for effective infection prevention and control programmes: new WHO evidence-based recommendations. Antimicrob Resist Infect Control 2017;6:6.CrossRefGoogle ScholarPubMed
Carrico, R. APIC Text of Infection Control and Epidemiology, 3rd edition. Washington, SC: APIC; 2009.Google Scholar
Fitzgerald, JR, Musser, JM. Evolutionary genomics of pathogenic bacteria. Trends Microbiol 2001;9:547–553.CrossRefGoogle ScholarPubMed
Bonten, MJ, Hayden, MK, Nathan, C, Rice, TW, Weinstein, RA. Stability of vancomycin-resistant enterococcal genotypes isolated from long-term-colonized patients. J Infect Dis 1998;177:378–382.CrossRefGoogle ScholarPubMed
Spiers, AJ, Buckling, A, Rainey, PB. The causes of Pseudomonas diversity. Microbiology 2000;146:2345–2350.CrossRefGoogle ScholarPubMed
Paulin-Curlee, GG, Singer, RS, Sreevatsan, S, et al. Genetic diversity of mastitis-associated Klebsiella pneumoniae in dairy cows. J Dairy Sci 2007;90:3681–3689.CrossRefGoogle ScholarPubMed
Shapiro, BJ, Polz, MF. Ordering microbial diversity into ecologically and genetically cohesive units. Trends in Microbiol 2014;22:235–247.CrossRefGoogle ScholarPubMed
Long, SW, Beres, SB, Olsen, RJ, Musser, JM. Absence of patient-to-patient intrahospital transmission of Staphylococcus aureus as determined by whole-genome sequencing. MBio 2014;5(5):e01692–14.CrossRefGoogle ScholarPubMed
Raven, KE, Gouliouris, T, Brodrick, H, et al. Complex routes of nosocomial vancomycin-resistant Enterococcus faecium transmission revealed by genome sequencing. Clin Infect Dis 2017;64:886–893.CrossRefGoogle ScholarPubMed
Chen, D, Xu, L, Fortunato-Habib, M, et al. Genomic sequencing and clinical data integration for next-generation infection prevention. Abstract no. 72682. Infectious Disease ID Week 2018, San Francisco, CA; 2018.Google Scholar
Cosgrove, SE. The relationship between antimicrobial resistance and patient outcomes: mortality, length of hospital stay, and health care costs. Clin Infect Dis 2006;42 Suppl 2:S82–S89.CrossRefGoogle ScholarPubMed
Stone, PW, Gupta, A, Loughrey, M, et al. Attributable costs and length of stay of an extended-spectrum beta-lactamase-producing Klebsiella pneumoniae outbreak in a neonatal intensive care unit. Infect Control Hosp Epidemiol 2003;24:601–606.CrossRefGoogle Scholar
Barlam, TF, Cosgrove, SE, Abbo, LM, et al. Implementing an antibiotic stewardship program: guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clin Infect Dis 2016;62:e51–e77.CrossRefGoogle Scholar
Ostrowsky, B, Banerjee, R, Bonomo, RA, et al. Infectious diseases physicians: leading the way in antimicrobial stewardship. Clin Infect Dis 2018;66:995–1003.CrossRefGoogle ScholarPubMed
Supplementary material: PDF

Ward et al. supplementary material

Ward et al. supplementary material 1

Download Ward et al. supplementary material(PDF)
PDF 426.4 KB