ArticleFrontiers in cardiovascular medicine2022
Long-term single-lead electrocardiogram monitoring to detect new-onset postoperative atrial fibrillation in patients after cardiac surgery.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Machine learning and Regression-Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Long-term continuous monitoring of wearable ECG signals for atrial fibrillation detection.Scientific reports · 2026Article
- A comprehensive survey of artificial intelligence methods for cardiovascular disease detection: Recent advances and future challenges.MethodsX · 2025Review
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Shaping the future of cardiac interventions and cardiac surgeries: The impact of virtual reality and artificial intelligence.Global cardiology science & practice · 2025Review
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- Development and internal validation of an OPCABG-specific prediction model for postoperative atrial fibrillation in Chinese patients: a retrospective cohort study.BMC cardiovascular disorders · 2025Article
- Artificial intelligence to predict biomarkers for new-onset atrial fibrillation after coronary artery bypass grafting.Turk gogus kalp damar cerrahisi dergisi · 2025Article
- Postoperative Atrial Fibrillation: A Review.Biomedicines · 2024Review
- Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet.Sensors (Basel, Switzerland) · 2024Article
- Postoperative atrial fibrillation (POAF) after cardiac surgery: clinical practice review.Journal of thoracic disease · 2024Review
- Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.Heliyon · 2023Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Postoperative atrial fibrillation (POAF) is often associated with serious complications. In this study, we collected long-term single-lead electrocardiograms (ECGs) of patients with preoperative sinus rhythm to build statistical models and machine learning models to predict POAF. Methods: All patients with preoperative sinus rhythm who underwent cardiac surgery were enrolled and we collected long-term ECG data 24 h before surgery and 7 days after surgery by single-lead ECG. The patients were divided into a POAF group a no-POAF group. A clinical model and a clinical + ECG model were constructed. The ECG parameters were designed and support vector machine (SVM) was selected to build a machine learning model and evaluate its prediction efficiency. Results: A total of 100 patients were included. The detection rate of POAF in long-term ECG monitoring was 31% and that in conventional monitoring was 19%. We calculated 7 P-wave parameters, Pmax (167 ± 31 ms vs. 184 ± 37 ms, Conclusion: Long-term ECG monitoring could significantly improve the detection rate of POAF. The clinical + ECG model and the machine learning model based on P-wave parameters can predict POAF.
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