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Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study
Preterm birth is the leading cause of death worldwide in children under five years. Due to its complex multifactorial nature, prediction is a challenge. Current research is aiming to develop accurate predictive models using patient history, ultrasound and biochemical markers. Volatile organic compou...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376243/ https://www.ncbi.nlm.nih.gov/pubmed/32699319 http://dx.doi.org/10.1038/s41598-020-69142-4 |
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author | Lacey, Lauren Daulton, Emma Wicaksono, Alfian Covington, James A. Quenby, Siobhan |
author_facet | Lacey, Lauren Daulton, Emma Wicaksono, Alfian Covington, James A. Quenby, Siobhan |
author_sort | Lacey, Lauren |
collection | PubMed |
description | Preterm birth is the leading cause of death worldwide in children under five years. Due to its complex multifactorial nature, prediction is a challenge. Current research is aiming to develop accurate predictive models using patient history, ultrasound and biochemical markers. Volatile organic compound (VOC) analysis is an approach, which has good diagnostic potential to predict many disease states. Analysis of VOCs can reflect both the microbiome and host response to a condition. We aimed to ascertain if VOC analysis of vaginal swabs, taken throughout pregnancy, could predict which women go on to deliver preterm. Our prospective observational cohort study demonstrates that VOC analysis of vaginal swabs, taken in the midtrimester, is a fair test (AUC 0.79) for preterm prediction, with a sensitivity of 0.66 (95%CI 0.56–0.75) and specificity 0.89 (95%CI 0.82–0.94). Using vaginal swabs taken closest to delivery, VOC analysis is a good test (AUC 0.84) for the prediction of preterm birth with a sensitivity of 0.73 (95%CI 0.64–0.81) and specificity of 0.90 (95%CI 0.82–0.95). Consequently, VOC analysis of vaginal swabs has potential to be used as a predictive tool. With further work it could be considered as an additional component in models for predicting preterm birth. |
format | Online Article Text |
id | pubmed-7376243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73762432020-07-24 Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study Lacey, Lauren Daulton, Emma Wicaksono, Alfian Covington, James A. Quenby, Siobhan Sci Rep Article Preterm birth is the leading cause of death worldwide in children under five years. Due to its complex multifactorial nature, prediction is a challenge. Current research is aiming to develop accurate predictive models using patient history, ultrasound and biochemical markers. Volatile organic compound (VOC) analysis is an approach, which has good diagnostic potential to predict many disease states. Analysis of VOCs can reflect both the microbiome and host response to a condition. We aimed to ascertain if VOC analysis of vaginal swabs, taken throughout pregnancy, could predict which women go on to deliver preterm. Our prospective observational cohort study demonstrates that VOC analysis of vaginal swabs, taken in the midtrimester, is a fair test (AUC 0.79) for preterm prediction, with a sensitivity of 0.66 (95%CI 0.56–0.75) and specificity 0.89 (95%CI 0.82–0.94). Using vaginal swabs taken closest to delivery, VOC analysis is a good test (AUC 0.84) for the prediction of preterm birth with a sensitivity of 0.73 (95%CI 0.64–0.81) and specificity of 0.90 (95%CI 0.82–0.95). Consequently, VOC analysis of vaginal swabs has potential to be used as a predictive tool. With further work it could be considered as an additional component in models for predicting preterm birth. Nature Publishing Group UK 2020-07-22 /pmc/articles/PMC7376243/ /pubmed/32699319 http://dx.doi.org/10.1038/s41598-020-69142-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lacey, Lauren Daulton, Emma Wicaksono, Alfian Covington, James A. Quenby, Siobhan Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
title | Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
title_full | Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
title_fullStr | Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
title_full_unstemmed | Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
title_short | Volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
title_sort | volatile organic compound analysis, a new tool in the quest for preterm birth prediction—an observational cohort study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7376243/ https://www.ncbi.nlm.nih.gov/pubmed/32699319 http://dx.doi.org/10.1038/s41598-020-69142-4 |
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