Path to precision: Prevention of post-operative atrial fibrillation

Rinku Skaria, Saman Parvaneh, Sophia Zhou, James Kim, Santana Wanjiru, Genoveffa Devers, John Konhilas, Zain Khalpey

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)2735-2746
Number of pages12
JournalJournal of Thoracic Disease
Issue number5
StatePublished - May 1 2020


  • Deep learning
  • Machine learning (ML)
  • Post-operative atrial fibrillation (POAF)

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine


Dive into the research topics of 'Path to precision: Prevention of post-operative atrial fibrillation'. Together they form a unique fingerprint.

Cite this