Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T05:46:20.243Z Has data issue: false hasContentIssue false
  • English
  • Français

Healthcare microenvironments define multidrug-resistant organism persistence

Published online by Cambridge University Press:  24 August 2021

Brendan J. Kelly Open the ORCID record for Brendan J. Kelly [Opens in a new window] ,
Selamawit Bekele ,
Sean Loughrey ,
Elizabeth Huang ,
Pam Tolomeo ,
Michael Z. David ,
Ebbing Lautenbach ,
Jennifer H. Han ,
Matthew J. Ziegler  and
for the CDC Prevention Epicenters Program
Brendan J. Kelly*
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Selamawit Bekele
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Sean Loughrey
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Elizabeth Huang
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Pam Tolomeo
Affiliation:
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Michael Z. David
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Ebbing Lautenbach
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Jennifer H. Han
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
Matthew J. Ziegler
Affiliation:
Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
*
Author for correspondence: Brendan J. Kelly, E-mail: brendank@pennmedicine.upenn.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Background:

Multidrug-resistant organisms (MDROs) colonizing the healthcare environment have been shown to contribute to risk for healthcare-associated infections (HAIs), with adverse effects on patient morbidity and mortality. We sought to determine how bacterial contamination and persistent MDRO colonization of the healthcare environment are related to the position of patients and wastewater sites.

Methods:

We performed a prospective cohort study, enrolling 51 hospital rooms at the time of admitting a patient with an eligible MDRO in the prior 30 days. We performed systematic sampling and MDRO culture of rooms, as well as 16S rRNA sequencing to define the environmental microbiome in a subset of samples.

Results:

The probability of detecting resistant gram-negative organisms, including Enterobacterales, Acinetobacter spp, and Pseudomonas spp, increased with distance from the patient. In contrast, Clostridioides difficile and methicillin-resistant Staphylococcus aureus were more likely to be detected close to the patient. Resistant Pseudomonas spp and S. aureus were enriched in these hot spots despite broad deposition of 16S rRNA gene sequences assigned to the same genera, suggesting modifiable factors that permit the persistence of these MDROs.

Conclusions:

MDRO hot spots can be defined by distance from the patient and from wastewater reservoirs. Evaluating how MDROs are enriched relative to bacterial DNA deposition helps to identify healthcare micro-environments and suggests how targeted environmental cleaning or design approaches could prevent MDRO persistence and reduce infection risk.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 43 , Issue 9 , September 2022 , pp. 1135 - 1141
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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

Buchan, BW, Graham, MB, Lindmair-Snell, J, et al. The relevance of sink proximity to toilets on the detection of Klebsiella pneumoniae carbapenemase inside sink drains. Am J Infection Control 2019;47:98–100.CrossRefGoogle ScholarPubMed
Mody, L, Washer, LL, Kaye, KS, et al . Multidrug-resistant organisms in hospitals: what is on patient hands and in their rooms? Clin Infect Dis 2019;69:1837–1844.CrossRefGoogle ScholarPubMed
Shams, AM, Rose, LJ, Edwards, JR, et al. Assessment of the overall and multidrug-resistant organism bioburden on environmental surfaces in healthcare facilities. Infect Control Hosp Epidemiol 2016;37:1426–1432.CrossRefGoogle ScholarPubMed
Weber, DJ, Rutala, WA. Understanding and preventing transmission of healthcare-associated pathogens due to the contaminated hospital environment. Infect Control Hosp Epidemiol 2013;34:449–452.CrossRefGoogle Scholar
Carling, PC. Wastewater drains: epidemiology and interventions in 23 carbapenem-resistant organism outbreaks. Infect Control Hosp Epidemiol 2018;39:972–979.CrossRefGoogle ScholarPubMed
Johnson, RC, Deming, C, Conlan, S, et al. Investigation of a cluster of sphingomonas koreensis infections. N Engl J Med 2018;379:2529–2539.CrossRefGoogle ScholarPubMed
Kizny Gordon, AE, Mathers, AJ, Cheong, EYL, et al. The hospital water environment as a reservoir for carbapenem-resistant organisms causing hospital-acquired infections—a systematic review of the literature. Clin Infect Dis 2017;64:1435–1444.CrossRefGoogle ScholarPubMed
Regev-Yochay, G, Smollan, G, Tal, I, et al. Sink traps as the source of transmission of OXA-48-producing serratia marcescens in an intensive care unit. Infect Control Hosp Epidemiol 2018;39:1307–1315.CrossRefGoogle Scholar
Chen, LF, Knelson, LP, Gergen, MF, et al. A prospective study of transmission of multidrug-resistant organisms (MDROs) between environmental sites and hospitalized patients—the TransFER study. Infect Control Hosp Epidemiol 2019;40:47–52.CrossRefGoogle ScholarPubMed
Freedberg, DE, Salmasian, H, Cohen, B, Abrams, JA, Larson, EL. Receipt of antibiotics in hospitalized patients and risk for clostridium difficile infection in subsequent patients who occupy the same bed. JAMA Intern Med 2016;176:1801–1808.CrossRefGoogle ScholarPubMed
Shaughnessy, MK, Micielli, RL, DePestel, DD, et al. Evaluation of hospital room assignment and acquisition of Clostridium difficile infection. Infect Control Hosp Epidemiol 2011;32:201–206.CrossRefGoogle ScholarPubMed
Weber, DJ, Anderson, D, Rutala, WA. The role of the surface environment in healthcare-associated infections. Curr Opin Infect Dis 2013;26:338–344.CrossRefGoogle ScholarPubMed
Lax, S, Sangwan, N, Smith, D, et al. Bacterial colonization and succession in a newly opened hospital. Science Translat Med 2017;9. doi: 10.1126/scitranslmed.aah6500.CrossRefGoogle Scholar
Callahan, BJ, Wong, J, Heiner, C, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucl Acids Res 2019;47:e103.CrossRefGoogle ScholarPubMed
Bolyen, E, Rideout, JR, Dillon, MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotech 2019;37:852–857.CrossRefGoogle ScholarPubMed
McDonald, D, Price, MN, Goodrich, J, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 2012;6:610–618.CrossRefGoogle ScholarPubMed
Bokulich, NA, Kaehler, BD, Rideout, JR, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 2018;6:90.CrossRefGoogle ScholarPubMed
Gloor, GB, Macklaim, JM, Pawlowsky-Glahn, V, Egozcue, JJ. Microbiome data sets are compositional: and this is not optional. Front Microbiol 2017;8:2224.CrossRefGoogle Scholar
R Core Team. R: A language and environment for statistical computing. R Project website. https://www.r-project.org/. Published 2018. Accessed July 19, 2021.Google Scholar
Wickham, H. Ggplot2: Elegant Graphics for Data Analysis, 3rd printing, 2010 edition. New York: Springer International; 2016.CrossRefGoogle Scholar
Carpenter, B, Gelman, A, Hoffman, MD, et al. Stan: a probabilistic programming language. J Stat Softw 2017;76:1–32.CrossRefGoogle Scholar
Bürkner, P-C. Brms: an R package for bayesian multilevel models using stan. J Stat Softw 2017;80:1–28.CrossRefGoogle Scholar
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan, 1st edition. Boca Raton, FL: CRC Press; 2016.Google Scholar
Gabry, J, Simpson, D, Vehtari, A, Betancourt, M, Gelman, A. Visualization in bayesian workflow. J Roy Stat Soc A 2019;182:389–402.CrossRefGoogle Scholar
Gelman, A, Vehtari, A, Simpson, D, et al. Bayesian workflow. Arxiv website. https://arxiv.org/abs/2011.01808. Published November 2020. Accessed July 19, 2021.Google Scholar
Supplementary material: File

Kelly et al. supplementary material

Kelly et al. supplementary material

Download Kelly et al. supplementary material(File)
File 25.7 KB