AI Article Synopsis
- Development of post-operative atrial fibrillation (POAF) after open-heart surgery poses a serious clinical and economic challenge, with incidence rates still high at 25-40%.
- Early research mainly concentrated on using electrocardiograms to spot arrhythmias and assessing pre-operative risk factors, but progress has stalled, highlighting the need for deeper understanding of its causes.
- Recent advancements in machine learning algorithms and high-throughput sequencing offer new potential for predicting POAF in real-time, urging a collaborative approach to integrate diverse data for improved diagnosis and treatment opportunities.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330352 | PMC |
| http://dx.doi.org/10.21037/jtd-19-3875 | DOI Listing |
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