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Predicting probability of perirectal colonization with carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) at hospital unit admission

Published online by Cambridge University Press:  27 March 2019

Katherine E. Goodman Open the ORCID record for Katherine E. Goodman [Opens in a new window] ,
Patricia J. Simner ,
Eili Y. Klein ,
Abida Q. Kazmi ,
Avinash Gadala ,
Matthew F. Toerper ,
Scott Levin ,
Pranita D. Tamma ,
Clare Rock  and
Sara E. Cosgrove
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Katherine E. Goodman*
Affiliation:
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
Patricia J. Simner
Affiliation:
Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
Eili Y. Klein
Affiliation:
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland The Center for Disease Dynamics, Economics, and Policy, Washington, DC
Abida Q. Kazmi
Affiliation:
Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
Avinash Gadala
Affiliation:
Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland
Matthew F. Toerper
Affiliation:
Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
Scott Levin
Affiliation:
Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
Pranita D. Tamma
Affiliation:
Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
Clare Rock
Affiliation:
Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, Maryland
Sara E. Cosgrove
Affiliation:
Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, Maryland
Lisa L. Maragakis
Affiliation:
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, Maryland
Aaron M. Milstone
Affiliation:
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
for the CDC Prevention Epicenters Program
Affiliation:
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland The Center for Disease Dynamics, Economics, and Policy, Washington, DC Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland Division of Infectious Diseases, Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, Maryland
the CDC MIND-Healthcare Program
Affiliation:
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland The Center for Disease Dynamics, Economics, and Policy, Washington, DC Department of Hospital Epidemiology and Infection Control, The Johns Hopkins Health System, Baltimore, Maryland Division of Infectious Diseases, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland Division of Infectious Diseases, Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, Maryland
*
Author for correspondence: Katherine E. Goodman, Email: Kgoodma7@jhu.edu.
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Abstract

Background:

Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.

Objective:

Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.

Methods:

We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.

Results:

Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.

Conclusions:

In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 5 , May 2019 , pp. 541 - 550
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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