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1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine

BACKGROUND: In this study, we assessed the diagnostic yield of metagenomics urine sample testing in patients with urological symptoms. METHODS: We conducted metagenomic analysis on 69 consecutive unbiased female patients, by sequencing their DNA using the KAPA HyperPlus library construction with nex...

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Autores principales: Valencia, C Alexander, Baugher, David, Larsen, Alexander, Chen, Alvin, Icenhour, Crystal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777885/
http://dx.doi.org/10.1093/ofid/ofaa439.1859
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author Valencia, C Alexander
Baugher, David
Larsen, Alexander
Chen, Alvin
Icenhour, Crystal
author_facet Valencia, C Alexander
Baugher, David
Larsen, Alexander
Chen, Alvin
Icenhour, Crystal
author_sort Valencia, C Alexander
collection PubMed
description BACKGROUND: In this study, we assessed the diagnostic yield of metagenomics urine sample testing in patients with urological symptoms. METHODS: We conducted metagenomic analysis on 69 consecutive unbiased female patients, by sequencing their DNA using the KAPA HyperPlus library construction with next-generation sequencing (Nextseq500, Illumina) and reads were analyzed using Xplore-Patho®, an analytical system that permits the detection of 37,000+ microorganisms, including over 12,000 known pathogens, and examined report summaries written by infectious disease experts to obtain a diagnostic yield. In addition, infectious disease expert analysis was contrasted with a natural language (NLP) pathogen detection system to investigate its accuracy. RESULTS: In the expert data summaries, a total of 95% of the patients tested had at least one pathogen identified by metagenomics as a potential explanation of their urological symptoms and these results were binned into four categories: 1) 51% of infection likely, 2) 4% of infection possible, 3) 26% of low-grade infection likely and 4) 14% of low-grade infection possible. Data from healthy controls was used in conjunction with an NLP pathogen detection pipeline and compared to infectious disease expert summaries. The NLP pathogen algorithm detected that at least 97% of samples had one pathogen which was more than 5 standard deviations from the abundance of that pathogen in healthy controls, and least 84% had 2 or more pathogens. These diagnostic percentages were consistent with the infectious disease expert summaries. The NLP algorithm had access to a large database derived from PubMed articles and it was found that several relevant uropathogens were not mentioned in report summaries. For example, one well-documented uropathogen was present in 13 samples, but was not mentioned in any report summaries. CONCLUSION: In conclusion, this study demonstrated the high diagnostic yield in females with urological symptoms following metagenomic analysis and the ability of NLP to enhance the sensitivity of reportable pathogens. DISCLOSURES: All Authors: No reported disclosures
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spelling pubmed-77778852021-01-07 1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine Valencia, C Alexander Baugher, David Larsen, Alexander Chen, Alvin Icenhour, Crystal Open Forum Infect Dis Poster Abstracts BACKGROUND: In this study, we assessed the diagnostic yield of metagenomics urine sample testing in patients with urological symptoms. METHODS: We conducted metagenomic analysis on 69 consecutive unbiased female patients, by sequencing their DNA using the KAPA HyperPlus library construction with next-generation sequencing (Nextseq500, Illumina) and reads were analyzed using Xplore-Patho®, an analytical system that permits the detection of 37,000+ microorganisms, including over 12,000 known pathogens, and examined report summaries written by infectious disease experts to obtain a diagnostic yield. In addition, infectious disease expert analysis was contrasted with a natural language (NLP) pathogen detection system to investigate its accuracy. RESULTS: In the expert data summaries, a total of 95% of the patients tested had at least one pathogen identified by metagenomics as a potential explanation of their urological symptoms and these results were binned into four categories: 1) 51% of infection likely, 2) 4% of infection possible, 3) 26% of low-grade infection likely and 4) 14% of low-grade infection possible. Data from healthy controls was used in conjunction with an NLP pathogen detection pipeline and compared to infectious disease expert summaries. The NLP pathogen algorithm detected that at least 97% of samples had one pathogen which was more than 5 standard deviations from the abundance of that pathogen in healthy controls, and least 84% had 2 or more pathogens. These diagnostic percentages were consistent with the infectious disease expert summaries. The NLP algorithm had access to a large database derived from PubMed articles and it was found that several relevant uropathogens were not mentioned in report summaries. For example, one well-documented uropathogen was present in 13 samples, but was not mentioned in any report summaries. CONCLUSION: In conclusion, this study demonstrated the high diagnostic yield in females with urological symptoms following metagenomic analysis and the ability of NLP to enhance the sensitivity of reportable pathogens. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2020-12-31 /pmc/articles/PMC7777885/ http://dx.doi.org/10.1093/ofid/ofaa439.1859 Text en © The Author 2020. 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 Poster Abstracts
Valencia, C Alexander
Baugher, David
Larsen, Alexander
Chen, Alvin
Icenhour, Crystal
1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine
title 1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine
title_full 1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine
title_fullStr 1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine
title_full_unstemmed 1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine
title_short 1681. Female Urinary Metagenomic Analysis and Natural Language Processing Enhances the Infectious Diagnostic Yield in Precision Medicine
title_sort 1681. female urinary metagenomic analysis and natural language processing enhances the infectious diagnostic yield in precision medicine
topic Poster Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777885/
http://dx.doi.org/10.1093/ofid/ofaa439.1859
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