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The damage response framework and infection prevention: From concept to bedside

Published online by Cambridge University Press:  09 January 2020

Emily J. Godbout Open the ORCID record for Emily J. Godbout [Opens in a new window] ,
Theresa Madaline ,
Arturo Casadevall ,
Gonzalo Bearman  and
Liise-anne Pirofski
Emily J. Godbout*
Affiliation:
Division of Pediatric Infectious Diseases, Department of Pediatrics, Children’s Hospital of Richmond at Virginia Commonwealth University, Richmond, Virginia
Theresa Madaline
Affiliation:
Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, New York City, New York
Arturo Casadevall
Affiliation:
Department of Microbiology and Immunology, Johns Hopkins University School of Public Health, Baltimore, Maryland
Gonzalo Bearman
Affiliation:
Division of Infectious Diseases, Department of Medicine, Virginia Commonwealth University, Richmond, Virginia
Liise-anne Pirofski
Affiliation:
Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, New York City, New York
*
Author for correspondence: Emily J. Godbout, E-mail: [email protected]
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Abstract

Hospital-acquired infections remain a common cause of morbidity and mortality despite advances in infection prevention through use of bundles, environmental cleaning, antimicrobial stewardship, and other best practices. Current prevention strategies and further hospital-acquired infection reduction are limited by lack of recognition of the role that host–microbe interactions play in susceptibility and by the inability to analyze multiple risk factors in real time to accurately predict the likelihood of a hospital-acquired infection before it occurs and to inform medical decision making. Herein, we examine the value of incorporating the damage-response framework and host attributes that determine susceptibility to infectious diseases known by the acronym MISTEACHING (ie, microbiome, immunity, sex, temperature, environment, age, chance, history, inoculum, nutrition, genetics) into infection prevention strategies using machine learning to drive decision support and patient-specific interventions.

Type
Commentary
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 3 , March 2020 , pp. 337 - 341
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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