Dataset Information

Regional differences in the predictors of acute electrical reconnection following high-power pulmonary vein isolation for paroxysmal atrial fibrillation.

ABSTRACT:

Background

Acute pulmonary vein reconnection (PVR) is associated with long procedure times and large radiofrequency (RF) energy delivery during pulmonary vein isolation (PVI). Although the efficacy of high-power PVI (HP-PVI) has been recently established, the determinants of acute PVR following HP-PVI remain unclear.

Methods

We evaluated data on 62 patients with paroxysmal atrial fibrillation undergoing unipolar signal modification (USM)-guided HP-PVI. A 50-W RF wave was applied for 3-5 seconds after USM. In the segments adjacent to the esophagus (SAEs), the RF time was limited to 5 seconds. Each circle was subdivided into six regions (segments), and the possible predictors of acute PVR, including minimum contact force (CF_min), minimum force-time integral (FTI_min), minimum ablation index (AI_min), minimum impedance drop (Imp-min), and maximum inter-lesion distance (ILD_max), were assessed in each segment.

Results

We investigated 1162 ablations in 744 segments (including 124 SAEs). Acute PVR was observed in 21 (17%) SAEs and 43 (7%) other segments (P = .001). The acute PVR segments were characterized by significantly lower CF_min, FTI_min, AI_min, and Imp-min values in the segments other than the SAEs and larger ILD_max values in the SAEs. Furthermore, lower Imp-min and larger ILD_max values independently predicted acute PVR in the segments other than the SAEs and SAEs (odds ratios 0.90 and 1.39 respectively). Acute PVR was not significantly associated with late atrial fibrillation recurrence.

Conclusions

Avoiding PVR remains a challenge in HP-PVI cases, but it might be resolved by setting the optimal target impedance drop and lesion distance values.

SUBMITTER: Yazaki K

PROVIDER: S-EPMC8485794 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI.MethodsThis single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI.ResultsIn our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47).ConclusionsML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.