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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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References

Casadevall, A, Pirofski, LA.Host–pathogen interactions: redefining the basic concepts of virulence and pathogenicity. Infect Immun 1999;67:3703–3713.CrossRefGoogle ScholarPubMed
Casadevall, A, Pirofski, LA.The damage–response framework of microbial pathogenesis. Nat Rev Microbiol 2003;1:17–24.CrossRefGoogle ScholarPubMed
Casadevall, A, Pirofski, LA.What is a host? Attributes of individual susceptibility. Infect Immun 2018;86:e00636–17.Google ScholarPubMed
Casadevall, A, Pirofski, LA.Ditch the term pathogen. Nature 2014;516:165–166.CrossRefGoogle ScholarPubMed
Casadevall, A, Pirofski, LA.The damage–response framework of microbial pathogenesis. Nat Rev Microbiol 2003;1:17–24.CrossRefGoogle ScholarPubMed
Casadevall, A, Pirofski, LA.Benefits and costs of animal virulence for microbes. MBio 2019;10:e00863–19.CrossRefGoogle ScholarPubMed
Casadevall, A, Pirofski, LA.What is a host? Incorporating the microbiota into the damage-response framework. Infect Immun 2015;83:2–7.CrossRefGoogle ScholarPubMed
Pirofski, LA, Casadevall, A.Immune-mediated damage completes the parabola: Cryptococcus neoformans pathogenesis can reflect the outcome of a weak or strong immune response. MBio 2017;8:e02063–17.CrossRefGoogle ScholarPubMed
HAI data and statistics. Centers for Disease Control and Prevention website. https://www.cdc.gov/hai/surveillance/. Published 2016. Accessed April 18, 2019.Google Scholar
Magill, SS, O’Leary, E, Janelle, SJ, et al.Changes in prevalence of healthcare-associated infections in US hospitals. N Engl J Med 2018;379:1732–1744.CrossRefGoogle Scholar
Schreiber, PW, Sax, H, Wolfensberger, A, Clack, L, Kuster, SP.The preventable proportion of healthcare-associated infections 2005–2016: systematic review and meta-analysis. Infect Control Hosp Epidemiol 2018;39:1277–1295.CrossRefGoogle ScholarPubMed
Roth, JA, Battegay, M, Juchler, F, Vogt, JE, Widmer, AF.Introduction to machine learning in digital healthcare epidemiology. Infect Control Hosp Epidemiol 2018;39:1457–1462.CrossRefGoogle ScholarPubMed
Oh, J, Makar, M, Fusco, C, et al.A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers. Infect Control Hosp Epidemiol 2018;39:425–433.CrossRefGoogle ScholarPubMed
Sanger, PC, van Ramshorst, GH, Mercan, E, et al.A prognostic model of surgical site infection using daily clinical wound assessment. J Am Coll Surg 2016;223:259–270.e2.CrossRefGoogle ScholarPubMed
Bartz-Kurycki, MA, Green, C, Anderson, KT, et al.Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm. Am J Surg 2018;216:764–777.CrossRefGoogle ScholarPubMed
Escobar, GJ, Baker, JM, Kipnis, P, et al.Prediction of recurrent Clostridium difficile infection using comprehensive electronic medical records in an integrated healthcare delivery system. Infect Control Hosp Epidemiol 2017;38:1196–1203.CrossRefGoogle Scholar
Peiffer-Smadja, N, Rawson, TM, Ahmad, R, et al.Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clin Microbiol Infect 2019; pii:S1198:30494-X.Google ScholarPubMed
Wenzel, RP.Surgical site infections and the microbiome: an updated perspective. Infect Control Hosp Epidemiol 2019;40:590–596.CrossRefGoogle ScholarPubMed
Bode, LGM, Kluytmans, JAJW, Wertheim, HFL, et al.Preventing surgical-site infections in nasal carriers of Staphylococcus aureus. N Engl J Med 2010;362:9–17.CrossRefGoogle ScholarPubMed
Climo, MW, Yokoe, DS, Warren, DK, et al.Effect of daily chlorhexidine bathing on hospital-acquired infection. N Engl J Med 2013;368:533–542.CrossRefGoogle ScholarPubMed
Huang, SS, Septimus, E, Kleinman, K, et al.Targeted versus universal decolonization to prevent ICU infection. N Engl J Med 2013;368:2255–2265.CrossRefGoogle ScholarPubMed
Van Rijen, M, Bonten, M, Wenzel, R, Kluytmans, J.Mupirocin ointment for preventing Staphylococcus aureus infections in nasal carriers. Cochrane Database Syst Rev 2008:CD006216.Google ScholarPubMed
Thomas, C, Stevenson, M, Riley, T V.Antibiotics and hospital-acquired Clostridium difficile–associated diarrhoea: a systematic review. J Antimicrob Chemother 2003;51:1339–1350.CrossRefGoogle ScholarPubMed
Loo, VG, Bourgault, AM, Poirier, L, et al.Host and pathogen factors for Clostridium difficile infection and colonization. N Engl J Med 2011;365:1693–1703.CrossRefGoogle ScholarPubMed
Keith, JW, Pamer, EG.Enlisting commensal microbes to resist antibiotic-resistant pathogens. J Exp Med 2019;216:10–19.CrossRefGoogle ScholarPubMed
Relman, DA, Lipsitch, M.Microbiome as a tool and a target in the effort to address antimicrobial resistance. Proc Natl Acad Sci 2018;115:12902–12910.CrossRefGoogle Scholar
Kokai-Kun, JF, Roberts, T, Coughlin, O, et al.Use of ribaxamase (SYN-004), a β-lactamase, to prevent Clostridium difficile infection in β-lactam–treated patients: a double-blind, phase 2b, randomised placebo-controlled trial. Lancet Infect Dis 2019;19:487–496.CrossRefGoogle ScholarPubMed
He, J, Li, Y, Zhang, H, et al.Immune function assay (ImmuKnow) as a predictor of allograft rejection and infection in kidney transplantation. Clin Transplant 2013;27:E351–E358.CrossRefGoogle ScholarPubMed
Seymour, CW, Kennedy, JN, Wang, S, et al.Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 2019;321:2003–2017.CrossRefGoogle ScholarPubMed
Kelly, CP, Chen, X, Williams, D, et al.Host immune markers distinguish clostridioides difficile infection from asymptomatic carriage and non–C. difficile diarrhea. Clin Infect Dis April 2019;pii:ciz330.Google Scholar
Drewry, AM, Fuller, BM, Bailey, TC, Hotchkiss, RS.Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case–control study. Crit Care 2013;17:R200.CrossRefGoogle ScholarPubMed
Anderson, DJ, Podgorny, K, Berríos-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
Sessler, DI.Complications and treatment of mild hypothermia. Anesthesiology 2001;95:531–543.CrossRefGoogle ScholarPubMed
Melling, AC, Ali, B, Scott, EM, Leaper, DJ.Effects of preoperative warming on the incidence of wound infection after clean surgery: a randomised controlled trial. Lancet 2001;358:876–880.CrossRefGoogle ScholarPubMed
Duval, A, Obadia, T, Boëlle, PY, et al.Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings. PLoS Comput Biol 2019;15:e1006496.CrossRefGoogle Scholar
Katona, P, Katona-Apte, J. The interaction between nutrition and infection. Clin Infect Dis 2008;46:1582–1588.CrossRefGoogle ScholarPubMed
Meijs, AP, Koek, MBG, Vos, MC, Geerlings, SE, Vogely, HC, De Greeff, SC.The effect of body mass index on the risk of surgical site infection. Infect Control Hosp Epidemiol 2019;40:991–996.CrossRefGoogle ScholarPubMed
Kerkhoffs, GMMJ, Servien, E, Dunn, W, Dahm, D, Bramer, JAM, Haverkamp, D.The influence of obesity on the complication rate and outcome of total knee arthroplasty. J Bone Jt Surg 2012;94:1839–1844.CrossRefGoogle ScholarPubMed