Why the study?
Postoperative atrial fibrillation affects 20 to 50% of cardiac surgery patients and accurate prediction remains challenging despite known risk factors.
Does continuous ECG patch monitoring combined with deep learning algorithms improve early risk stratification and near real-time prediction of postoperative atrial fibrillation in cardiac surgery patients?
Observational (n=20)
Does continuous ECG patch monitoring combined with deep learning algorithms improve early risk stratification and near real-time prediction of postoperative atrial fibrillation in cardiac surgery patients?
Effect estimate: AUC 0.95
Deep learning algorithms applied to continuous wearable ECG patch data can accurately predict postoperative atrial fibrillation up to an hour in advance with an AUC of 0.95.
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Ramezani et al. (2025) conducted an observational in Postoperative atrial fibrillation (n=20). Continuous multimodal ECG patch monitoring with deep learning algorithms was evaluated on Hour-ahead forecasting of atrial fibrillation (AUC 0.95).
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