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Path to precision: prevention of post-operative atrial fibrillation
Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25–40%. Early work focused on detecting arrhythmias from electrocardiograms as wel...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330352/ https://www.ncbi.nlm.nih.gov/pubmed/32642182 http://dx.doi.org/10.21037/jtd-19-3875 |
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author | Skaria, Rinku Parvaneh, Saman Zhou, Sophia Kim, James Wanjiru, Santana Devers, Genoveffa Konhilas, John Khalpey, Zain |
author_facet | Skaria, Rinku Parvaneh, Saman Zhou, Sophia Kim, James Wanjiru, Santana Devers, Genoveffa Konhilas, John Khalpey, Zain |
author_sort | Skaria, Rinku |
collection | PubMed |
description | Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25–40%. Early work focused on detecting arrhythmias from electrocardiograms as well as identifying pre-operative risk factors from medical records. However, further progress has been stagnant, and a deeper understanding of pathogenesis and significant influences is warranted. With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time. Integration of multimodal heterogeneous data and application of ML can generate a paradigm shift for diagnosis and treatment. This will require a concerted effort to consolidate and streamline real-time data. Herein, we will review the current literature and emerging opportunities aimed at predictive targets and new insights into the mechanisms underlying long-term sequelae of POAF. |
format | Online Article Text |
id | pubmed-7330352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-73303522020-07-07 Path to precision: prevention of post-operative atrial fibrillation Skaria, Rinku Parvaneh, Saman Zhou, Sophia Kim, James Wanjiru, Santana Devers, Genoveffa Konhilas, John Khalpey, Zain J Thorac Dis Review Article Development of post-operative atrial fibrillation (POAF) following open-heart surgery is a significant clinical and economic burden. Despite advancements in medical therapies, the incidence of POAF remains elevated at 25–40%. Early work focused on detecting arrhythmias from electrocardiograms as well as identifying pre-operative risk factors from medical records. However, further progress has been stagnant, and a deeper understanding of pathogenesis and significant influences is warranted. With the advent of more complex machine learning (ML) algorithms and high-throughput sequencing, we have an unprecedented ability to capture and predict POAF in real-time. Integration of multimodal heterogeneous data and application of ML can generate a paradigm shift for diagnosis and treatment. This will require a concerted effort to consolidate and streamline real-time data. Herein, we will review the current literature and emerging opportunities aimed at predictive targets and new insights into the mechanisms underlying long-term sequelae of POAF. AME Publishing Company 2020-05 /pmc/articles/PMC7330352/ /pubmed/32642182 http://dx.doi.org/10.21037/jtd-19-3875 Text en 2020 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Review Article Skaria, Rinku Parvaneh, Saman Zhou, Sophia Kim, James Wanjiru, Santana Devers, Genoveffa Konhilas, John Khalpey, Zain Path to precision: prevention of post-operative atrial fibrillation |
title | Path to precision: prevention of post-operative atrial fibrillation |
title_full | Path to precision: prevention of post-operative atrial fibrillation |
title_fullStr | Path to precision: prevention of post-operative atrial fibrillation |
title_full_unstemmed | Path to precision: prevention of post-operative atrial fibrillation |
title_short | Path to precision: prevention of post-operative atrial fibrillation |
title_sort | path to precision: prevention of post-operative atrial fibrillation |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330352/ https://www.ncbi.nlm.nih.gov/pubmed/32642182 http://dx.doi.org/10.21037/jtd-19-3875 |
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