Dataset Information

Earliest activation time is a good predictor of successful ablation of idiopathic outflow tract ventricular arrhythmias.

ABSTRACT:

Background

In idiopathic outflow tract ventricular arrhythmias (OT-VAs), identifying the site with the earliest activation time (EAT) using activation mapping is critical to eliminating the arrhythmogenic focus. However, the optimal EAT for predicting successful radiofrequency catheter ablation (RFCA) has not been established.

Hypothesis

To evaluate the association between EAT and successful RFCA in idiopathic OT-VAs and to determine the optimal cut-off value of EAT for successful ablation.

Methods

We retrospectively analyzed patients undergoing RFCA for idiopathic OT-VAs at a single center from January 2015 to December 2019.

Results

Acute procedural success was achieved in 168 patients (87.0%). Among these patients, 158 patients (81.9%) were classified in the clinical success group according to the recurrence of clinical VAs during median (Q1, Q3) follow-up (330 days [182, 808]). EAT was significantly earlier in the clinical success group compared with the recurrence (p = .006) and initial failure (p < .0001) groups. The optimal EAT cut-off value predicting clinical success was -30 ms in the right ventricular outflow tract (RVOT) with 77.4% sensitivity and 96.4% specificity. In all cases of successful ablation in the left ventricular outflow tract (LVOT), EAT in the RVOT was not earlier than -29 ms.

Conclusions

EAT in patients with successful catheter ablation was significantly earlier than that in patients with recurrence and initial failure. EAT earlier than -30 ms could be used as a key predictor of successful catheter ablation as well as an indicator of the need to shift focus from the RVOT to the LVOT.

SUBMITTER: Choi JH

PROVIDER: S-EPMC8027574 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.