Key result
ECG-based machine learning detects pulmonary vein drivers of AF with ~83% specificity.
Why the study?
PVI excludes PV-related drivers but is less effective in patients with additional extra-PV arrhythmogenic drivers, motivating noninvasive identification of driver locations.
Does a machine learning algorithm using 12-lead ECG features accurately predict atrial fibrillation driver location and acute pulmonary vein ablation success?
Does a machine learning algorithm using 12-lead ECG features accurately predict atrial fibrillation driver location and acute pulmonary vein ablation success?
A novel machine learning algorithm using 12-lead ECG features can noninvasively discriminate between PV and extra-PV drivers of AF, potentially identifying patients with high acute success rates for PVI.
Answers are grounded in this paper and cite their sources.
May aid noninvasive PV driver identification in AF; leaves open prospective validation before clinical adoption.
BACKGROUND: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. OBJECTIVES: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. METHODS: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). RESULTS: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. CONCLUSION: Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.
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Luongo et al. (2021) studied Atrial fibrillation (n=46). Machine learning-based classification of 12-lead ECG was evaluated on Detection of pulmonary vein drivers. A machine learning classifier using 12-lead ECG features detected pulmonary vein drivers of atrial fibrillation with 82.6% specificity and 73.9% sensitivity in a clinical dataset.
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