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Path to precision: prevention of post-operative atrial fibrillation.


ABSTRACT: 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.

SUBMITTER: Skaria R 

PROVIDER: S-EPMC7330352 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Path to precision: prevention of post-operative atrial fibrillation.

Skaria Rinku R   Parvaneh Saman S   Zhou Sophia S   Kim James J   Wanjiru Santana S   Devers Genoveffa G   Konhilas John J   Khalpey Zain Z  

Journal of thoracic disease 20200501 5


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  ...[more]

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