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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...

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Detalles Bibliográficos
Autores principales: Skaria, Rinku, Parvaneh, Saman, Zhou, Sophia, Kim, James, Wanjiru, Santana, Devers, Genoveffa, Konhilas, John, Khalpey, Zain
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
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
Descripción
Sumario: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.