Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection

ABSTRACT Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48...

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Autores principales:	Michael G. Dieterle, Rosemary Putler, D. Alexander Perry, Anitha Menon, Lisa Abernathy-Close, Naomi S. Perlman, Aline Penkevich, Alex Standke, Micah Keidan, Kimberly C. Vendrov, Ingrid L. Bergin, Vincent B. Young, Krishna Rao
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	biomarkers Clostridioides difficile Clostridium difficile machine learning predictive modeling cytokines Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/e87d93974f09471d90f6fe68dc80f3e2
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spelling	oai:doaj.org-article:e87d93974f09471d90f6fe68dc80f3e22021-11-15T15:56:46ZSystemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection10.1128/mBio.00180-202150-7511https://doaj.org/article/e87d93974f09471d90f6fe68dc80f3e22020-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.00180-20https://doaj.org/toc/2150-7511ABSTRACT Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile. Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment. IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.Michael G. DieterleRosemary PutlerD. Alexander PerryAnitha MenonLisa Abernathy-CloseNaomi S. PerlmanAline PenkevichAlex StandkeMicah KeidanKimberly C. VendrovIngrid L. BerginVincent B. YoungKrishna RaoAmerican Society for MicrobiologyarticlebiomarkersClostridioides difficileClostridium difficilemachine learningpredictive modelingcytokinesMicrobiologyQR1-502ENmBio, Vol 11, Iss 3 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	biomarkers Clostridioides difficile Clostridium difficile machine learning predictive modeling cytokines Microbiology QR1-502
spellingShingle	biomarkers Clostridioides difficile Clostridium difficile machine learning predictive modeling cytokines Microbiology QR1-502 Michael G. Dieterle Rosemary Putler D. Alexander Perry Anitha Menon Lisa Abernathy-Close Naomi S. Perlman Aline Penkevich Alex Standke Micah Keidan Kimberly C. Vendrov Ingrid L. Bergin Vincent B. Young Krishna Rao Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
description	ABSTRACT Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile. Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment. IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.
format	article
author	Michael G. Dieterle Rosemary Putler D. Alexander Perry Anitha Menon Lisa Abernathy-Close Naomi S. Perlman Aline Penkevich Alex Standke Micah Keidan Kimberly C. Vendrov Ingrid L. Bergin Vincent B. Young Krishna Rao
author_facet	Michael G. Dieterle Rosemary Putler D. Alexander Perry Anitha Menon Lisa Abernathy-Close Naomi S. Perlman Aline Penkevich Alex Standke Micah Keidan Kimberly C. Vendrov Ingrid L. Bergin Vincent B. Young Krishna Rao
author_sort	Michael G. Dieterle
title	Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_short	Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_full	Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_fullStr	Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_full_unstemmed	Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_sort	systemic inflammatory mediators are effective biomarkers for predicting adverse outcomes in <named-content content-type="genus-species">clostridioides difficile</named-content> infection
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/e87d93974f09471d90f6fe68dc80f3e2
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Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection

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