Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection

ABSTRACT There is a pressing need for biomarker-based models to predict mortality from and recurrence of Clostridioides difficile infection (CDI). Risk stratification would enable targeted interventions such as fecal microbiota transplant, antitoxin antibodies, and colectomy for those at highest ris...

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Autores principales:	Mayuresh M. Abhyankar, Jennie Z. Ma, Kenneth W. Scully, Andrew J. Nafziger, Alyse L. Frisbee, Mahmoud M. Saleh, Gregory R. Madden, Ann R. Hays, Mendy Poulter, William A. Petri
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	Clostridium difficile Clostridioides inflammation mortality predictive modeling Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/39ddbbd2dd9b412bb8754c0a4f511fdc
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spelling	oai:doaj.org-article:39ddbbd2dd9b412bb8754c0a4f511fdc2021-11-15T15:56:46ZImmune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection10.1128/mBio.00905-202150-7511https://doaj.org/article/39ddbbd2dd9b412bb8754c0a4f511fdc2020-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.00905-20https://doaj.org/toc/2150-7511ABSTRACT There is a pressing need for biomarker-based models to predict mortality from and recurrence of Clostridioides difficile infection (CDI). Risk stratification would enable targeted interventions such as fecal microbiota transplant, antitoxin antibodies, and colectomy for those at highest risk. Because severity of CDI is associated with the immune response, we immune profiled patients at the time of diagnosis. The levels of 17 cytokines in plasma were measured in 341 CDI inpatients. The primary outcome of interest was 90-day mortality. Increased tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), C-C motif chemokine ligand 5 (CCL-5), suppression of tumorigenicity 2 receptor (sST-2), IL-8, and IL-15 predicted mortality by univariate analysis. After adjusting for demographics and clinical characteristics, the mortality risk (as indicated by the hazard ratio [HR]) was higher for patients in the top 25th percentile for TNF-α (HR = 8.35, P = 0.005) and IL-8 (HR = 4.45, P = 0.01) and lower for CCL-5 (HR = 0.18, P ≤ 0.008). A logistic regression risk prediction model was developed and had an area under the receiver operating characteristic curve (AUC) of 0.91 for 90-day mortality and 0.77 for 90-day recurrence. While limited by being single site and retrospective, our work resulted in a model with a substantially greater predictive ability than white blood cell count. In conclusion, immune profiling demonstrated differences between patients in their response to CDI, offering the promise for precision medicine individualized treatment. IMPORTANCE Clostridioides difficile infection is the most common health care-associated infection in the United States with more than 20% patients experiencing symptomatic recurrence. The complex nature of host-bacterium interactions makes it difficult to predict the course of the disease based solely on clinical parameters. In the present study, we built a robust prediction model using representative plasma biomarkers and clinical parameters for 90-day all-cause mortality. Risk prediction based on immune biomarkers and clinical variables may contribute to treatment selection for patients as well as provide insight into the role of immune system in C. difficile pathogenesis.Mayuresh M. AbhyankarJennie Z. MaKenneth W. ScullyAndrew J. NafzigerAlyse L. FrisbeeMahmoud M. SalehGregory R. MaddenAnn R. HaysMendy PoulterWilliam A. PetriAmerican Society for MicrobiologyarticleClostridium difficileClostridioidesinflammationmortalitypredictive modelingMicrobiologyQR1-502ENmBio, Vol 11, Iss 3 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Clostridium difficile Clostridioides inflammation mortality predictive modeling Microbiology QR1-502
spellingShingle	Clostridium difficile Clostridioides inflammation mortality predictive modeling Microbiology QR1-502 Mayuresh M. Abhyankar Jennie Z. Ma Kenneth W. Scully Andrew J. Nafziger Alyse L. Frisbee Mahmoud M. Saleh Gregory R. Madden Ann R. Hays Mendy Poulter William A. Petri Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
description	ABSTRACT There is a pressing need for biomarker-based models to predict mortality from and recurrence of Clostridioides difficile infection (CDI). Risk stratification would enable targeted interventions such as fecal microbiota transplant, antitoxin antibodies, and colectomy for those at highest risk. Because severity of CDI is associated with the immune response, we immune profiled patients at the time of diagnosis. The levels of 17 cytokines in plasma were measured in 341 CDI inpatients. The primary outcome of interest was 90-day mortality. Increased tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), C-C motif chemokine ligand 5 (CCL-5), suppression of tumorigenicity 2 receptor (sST-2), IL-8, and IL-15 predicted mortality by univariate analysis. After adjusting for demographics and clinical characteristics, the mortality risk (as indicated by the hazard ratio [HR]) was higher for patients in the top 25th percentile for TNF-α (HR = 8.35, P = 0.005) and IL-8 (HR = 4.45, P = 0.01) and lower for CCL-5 (HR = 0.18, P ≤ 0.008). A logistic regression risk prediction model was developed and had an area under the receiver operating characteristic curve (AUC) of 0.91 for 90-day mortality and 0.77 for 90-day recurrence. While limited by being single site and retrospective, our work resulted in a model with a substantially greater predictive ability than white blood cell count. In conclusion, immune profiling demonstrated differences between patients in their response to CDI, offering the promise for precision medicine individualized treatment. IMPORTANCE Clostridioides difficile infection is the most common health care-associated infection in the United States with more than 20% patients experiencing symptomatic recurrence. The complex nature of host-bacterium interactions makes it difficult to predict the course of the disease based solely on clinical parameters. In the present study, we built a robust prediction model using representative plasma biomarkers and clinical parameters for 90-day all-cause mortality. Risk prediction based on immune biomarkers and clinical variables may contribute to treatment selection for patients as well as provide insight into the role of immune system in C. difficile pathogenesis.
format	article
author	Mayuresh M. Abhyankar Jennie Z. Ma Kenneth W. Scully Andrew J. Nafziger Alyse L. Frisbee Mahmoud M. Saleh Gregory R. Madden Ann R. Hays Mendy Poulter William A. Petri
author_facet	Mayuresh M. Abhyankar Jennie Z. Ma Kenneth W. Scully Andrew J. Nafziger Alyse L. Frisbee Mahmoud M. Saleh Gregory R. Madden Ann R. Hays Mendy Poulter William A. Petri
author_sort	Mayuresh M. Abhyankar
title	Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_short	Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_full	Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_fullStr	Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_full_unstemmed	Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection
title_sort	immune profiling to predict outcome of <named-content content-type="genus-species">clostridioides difficile</named-content> infection
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/39ddbbd2dd9b412bb8754c0a4f511fdc
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Immune Profiling To Predict Outcome of <named-content content-type="genus-species">Clostridioides difficile</named-content> Infection

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