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1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness

BACKGROUND: Host response-based diagnostics are an alternative to pathogen-based tests. Host response strategies include proteomic and transcriptomic approaches. Here, we compare three host response strategies for ARI diagnosis: Procalcitonin (PCT), a 3-protein panel, and an mRNA panel. METHODS: PCT...

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Autores principales: Ross, Melissa H, Henao, Ricardo, Burke, Thomas W, McClain, Micah T, Ginsburg, Geoffrey S, Woods, Chris W, Tsalik, Ephraim L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809287/
http://dx.doi.org/10.1093/ofid/ofz360.1194
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author Ross, Melissa H
Henao, Ricardo
Burke, Thomas W
McClain, Micah T
Ginsburg, Geoffrey S
Woods, Chris W
Tsalik, Ephraim L
Tsalik, Ephraim L
author_facet Ross, Melissa H
Henao, Ricardo
Burke, Thomas W
McClain, Micah T
Ginsburg, Geoffrey S
Woods, Chris W
Tsalik, Ephraim L
Tsalik, Ephraim L
author_sort Ross, Melissa H
collection PubMed
description BACKGROUND: Host response-based diagnostics are an alternative to pathogen-based tests. Host response strategies include proteomic and transcriptomic approaches. Here, we compare three host response strategies for ARI diagnosis: Procalcitonin (PCT), a 3-protein panel, and an mRNA panel. METHODS: PCT, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in a cohort of 286 participants presenting to one of the four Emergency Departments with ARI due to bacterial (n = 47), viral (n = 162), or noninfectious (n = 77) etiologies. Multinomial logistic regression and leave-one-out cross-validation were used to train and evaluate the protein and mRNA panels. Performance characteristics were calculated for each method, and their combination, for the ability to discriminate bacterial vs. non-bacterial infection and viral vs. nonviral infection. PCT was not evaluated for viral vs. nonviral discrimination since it does not discriminate viral and noninfectious etiologies. McNemar’s test was used to compare overall accuracy of mRNA and protein panels. RESULTS: For discriminating bacterial vs. non-bacterial etiologies, the mRNA panel had an AUC of 0.93 vs. 0.83 for both the protein panel and PCT. A model utilizing all three strategies was the same as mRNA alone. Using previously established cutoffs, overall accuracy was similar between mRNA and protein panels, but the protein panel had widely discordant sensitivity (43%) and specificity (92%). When selecting an optimal cutoff for the protein panel that balanced the two (82% and 73%, respectively), the mRNA panel had a significantly greater overall accuracy (P < 0.001). Similar results were found when discriminating viral vs. non-viral subjects: the mRNA panel (AUC = 0.93) outperformed the protein panel (AUC = 0.84). Combining the mRNA and protein panels was equivalent to the mRNA panel alone. CONCLUSION: A host-based gene expression signature is the most effective platform for classifying subjects with bacterial, viral, or noninfectious ARI. A gene expression approach, when translated to a clinically available platform, may facilitate diagnosis and clinical management of acute infectious diseases, mitigating antibiotic overuse. [Image: see text] DISCLOSURES: Ephraim L. Tsalik, MD, MHS, PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant.
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spelling pubmed-68092872019-10-28 1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness Ross, Melissa H Henao, Ricardo Burke, Thomas W McClain, Micah T Ginsburg, Geoffrey S Woods, Chris W Tsalik, Ephraim L Tsalik, Ephraim L Open Forum Infect Dis Abstracts BACKGROUND: Host response-based diagnostics are an alternative to pathogen-based tests. Host response strategies include proteomic and transcriptomic approaches. Here, we compare three host response strategies for ARI diagnosis: Procalcitonin (PCT), a 3-protein panel, and an mRNA panel. METHODS: PCT, a 3-protein panel (CRP, IP-10, TRAIL), and a host gene expression mRNA panel were measured in a cohort of 286 participants presenting to one of the four Emergency Departments with ARI due to bacterial (n = 47), viral (n = 162), or noninfectious (n = 77) etiologies. Multinomial logistic regression and leave-one-out cross-validation were used to train and evaluate the protein and mRNA panels. Performance characteristics were calculated for each method, and their combination, for the ability to discriminate bacterial vs. non-bacterial infection and viral vs. nonviral infection. PCT was not evaluated for viral vs. nonviral discrimination since it does not discriminate viral and noninfectious etiologies. McNemar’s test was used to compare overall accuracy of mRNA and protein panels. RESULTS: For discriminating bacterial vs. non-bacterial etiologies, the mRNA panel had an AUC of 0.93 vs. 0.83 for both the protein panel and PCT. A model utilizing all three strategies was the same as mRNA alone. Using previously established cutoffs, overall accuracy was similar between mRNA and protein panels, but the protein panel had widely discordant sensitivity (43%) and specificity (92%). When selecting an optimal cutoff for the protein panel that balanced the two (82% and 73%, respectively), the mRNA panel had a significantly greater overall accuracy (P < 0.001). Similar results were found when discriminating viral vs. non-viral subjects: the mRNA panel (AUC = 0.93) outperformed the protein panel (AUC = 0.84). Combining the mRNA and protein panels was equivalent to the mRNA panel alone. CONCLUSION: A host-based gene expression signature is the most effective platform for classifying subjects with bacterial, viral, or noninfectious ARI. A gene expression approach, when translated to a clinically available platform, may facilitate diagnosis and clinical management of acute infectious diseases, mitigating antibiotic overuse. [Image: see text] DISCLOSURES: Ephraim L. Tsalik, MD, MHS, PhD, Immunexpress: Consultant; Predigen, Inc.: Officer or Board Member, Research Grant. Oxford University Press 2019-10-23 /pmc/articles/PMC6809287/ http://dx.doi.org/10.1093/ofid/ofz360.1194 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Ross, Melissa H
Henao, Ricardo
Burke, Thomas W
McClain, Micah T
Ginsburg, Geoffrey S
Woods, Chris W
Tsalik, Ephraim L
Tsalik, Ephraim L
1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness
title 1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness
title_full 1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness
title_fullStr 1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness
title_full_unstemmed 1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness
title_short 1330. Evaluation of Multiple Host Response-Based Strategies to Classify Acute Respiratory Illness
title_sort 1330. evaluation of multiple host response-based strategies to classify acute respiratory illness
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809287/
http://dx.doi.org/10.1093/ofid/ofz360.1194
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