Left Atrial Wall Stress and the Long-Term Outcome of Catheter Ablation of Atrial Fibrillation: An Artificial Intelligence-Based Prediction of Atrial Wall Stress

Atrial stretch may contribute to the mechanism of atrial fibrillation (AF) recurrence after atrial fibrillation catheter ablation (AFCA). We tested whether the left atrial (LA) wall stress (LAW-stress[measured]) could be predicted by artificial intelligence (AI) using non-invasive parameters (LAW-stress[AI]) and whether rhythm outcome after AFCA could be predicted by LAW-stress[AI] in an independent cohort. Cohort 1 included 2223 patients, and cohort 2 included 658 patients who underwent AFCA. LAW-stress[measured] was calculated using the Law of Laplace using LA diameter by echocardiography, peak LA pressure measured during procedure, and LA wall thickness measured by customized software (AMBER) using computed tomography. The highest quartile (Q4) LAW-stress[measured] was predicted and validated by AI using non-invasive clinical parameters, including non-paroxysmal type of AF, age, presence of hypertension, diabetes, vascular disease, and heart failure, left ventricular ejection fraction, and the ratio of the peak mitral flow velocity of the early rapid filling to the early diastolic velocity of the mitral annulus (E/Em). We tested the AF/atrial tachycardia recurrence 3 months after the blanking period after AFCA using the LAW-stress[measured] and LAW-stress[AI] in cohort 1 and LAW-stress[AI] in cohort 2. LAW-stress[measured] was independently associated with non-paroxysmal AF (p < 0.001), diabetes (p = 0.012), vascular disease (p = 0.002), body mass index (p < 0.001), E/Em (p < 0.001), and mean LA voltage measured by electrogram voltage mapping (p < 0.001). The best-performing AI model had acceptable prediction power for predicting Q4-LAW-stress[measured] (area under the receiver operating characteristic curve 0.734). During 26.0 (12.0-52.0) months of follow-up, AF recurrence was significantly higher in the Q4-LAW-stress[measured] group [log-rank p = 0.001, hazard ratio 2.43 (1.21-4.90), p = 0.013] and Q4-LAW-stress[AI] group (log-rank p = 0.039) in cohort 1. In cohort 2, the Q4-LAW-stress[AI] group consistently showed worse rhythm outcomes (log-rank p < 0.001). A higher LAW-stress was associated with poorer rhythm outcomes after AFCA. AI was able to predict this complex but useful prognostic parameter using non-invasive parameters with moderate accuracy.ope

Clinical Outcomes of Computational Virtual Mapping-Guided Catheter Ablation in Patients With Persistent Atrial Fibrillation: A Multicenter Prospective Randomized Clinical Trial

Background: Clinical recurrence after atrial fibrillation catheter ablation (AFCA) still remains high in patients with persistent AF (PeAF). We investigated whether an extra-pulmonary vein (PV) ablation targeting the dominant frequency (DF) extracted from electroanatomical map-integrated AF computational modeling improves the AFCA rhythm outcome in patients with PeAF. Methods: In this open-label, randomized, multi-center, controlled trial, 170 patients with PeAF were randomized at a 1:1 ratio to the computational modeling-guided virtual DF (V-DF) ablation and empirical PV isolation (E-PVI) groups. We generated a virtual dominant frequency (DF) map based on the atrial substrate map obtained during the clinical AF ablation procedure using computational modeling. This simulation was possible within the time of the PVI procedure. V-DF group underwent extra-PV V-DF ablation in addition to PVI, but DF information was not notified to the operators from the core lab in the E-PVI group. Results: After a mean follow-up period of 16.3 ± 5.3 months, the clinical recurrence rate was significantly lower in the V-DF than with E-PVI group (P = 0.018, log-rank). Recurrences appearing as atrial tachycardias (P = 0.145) and the cardioversion rates (P = 0.362) did not significantly differ between the groups. At the final follow-up, sinus rhythm was maintained without any AADs in 74.7% in the V-DF group and 48.2% in the E-PVI group (P < 0.001). No significant difference was found in the major complication rates (P = 0.489) or total procedure time (P = 0.513) between the groups. The V-DF ablation was independently associated with a reduced AF recurrence after AFCA [hazard ratio: 0.51 (95% confidence interval: 0.30-0.88); P = 0.016]. Conclusions: The computational modeling-guided V-DF ablation improved the rhythm outcome of AFCA in patients with PeAF. Clinical Trial Registration: Clinical Research Information Service, CRIS identifier: KCT0003613.ope

Three-Dimensional Atrial Wall Thickness Measurement Algorithm From a Segmented Atrial Wall Using a Partial Differential Equation

Despite advancements in high-precision segmentation technology for computed tomographic angiography (CTA)-based cardiac wall segmentation, the accurate detection of the endocardial (Endo) and epicardial (Epi) boundaries remains a prerequisite for automated measurements of the cardiac wall thickness (WT). We proposed a novel algorithm for automated three-dimensional (3D) atrial WT (AWT) measurements, including an automatic Endo-Epi boundary detection. We detected the boundaries that were topologically indistinguishable due to an open geometry at the anatomical boundaries using the combined Convex hull and Poisson solver methods. The Laplace equation for the WT measurement was solved by a partial differential equation combining the two detected boundaries of the myocardial wall. We verified the robustness of our algorithm in mask images of the atrial wall that were separated from the CTA images of 20 patients and a phantom model. The accuracy of the automatically detected Endo-Epi boundaries was acceptable as compared to that manually extracted from the phantom model (Dice coefficient = 0.979). The 3D AWTs calculated by the novel automated method from the CTA images obtained from 20 patients with atrial fibrillation had <10% error as compared to the conventional manual AWT measurement method for comparing the regional WT in all locations except the left antral area. The proposed algorithm’s AWT detection time was 27.15±6.99 s per patient, which was 1/40 that of the conventional method. Consequently, our results showed that the proposed automatic 3D AWT measurement algorithm had the potential to significantly improve the efficiency of calculating the AWT while maintaining the existing level of accuracy.ope

Computational Modeling for Antiarrhythmic Drugs for Atrial Fibrillation According to Genotype

Background: The efficacy of antiarrhythmic drugs (AAD) can vary in patients with atrial fibrillation (AF), and the PITX2 gene affects the responsiveness of AADs. We explored the virtual AAD (V-AAD) responses between wild-type and PITX2 +/--deficient AF conditions by realistic in silico AF modeling. Methods: We tested the V-AADs in AF modeling integrated with patients' 3D-computed tomography and 3D-electroanatomical mapping, acquired in 25 patients (68% male, 59.8 ± 9.8 years old, 32.0% paroxysmal type). The ion currents for the PITX2 +/- deficiency and each AAD (amiodarone, sotalol, dronedarone, flecainide, and propafenone) were defined based on previous publications. Results: We compared the wild-type and PITX2 +/- deficiency in terms of the action potential duration (APD90), conduction velocity (CV), maximal slope of restitution (Smax), and wave-dynamic parameters, such as the dominant frequency (DF), phase singularities (PS), and AF termination rates according to the V-AADs. The PITX2 +/--deficient model exhibited a shorter APD90 (p < 0.001), a lower Smax (p < 0.001), mean DF (p = 0.012), PS number (p < 0.001), and a longer AF cycle length (AFCL, p = 0.011). Five V-AADs changed the electrophysiology in a dose-dependent manner. AAD-induced AFCL lengthening (p < 0.001) and reductions in the CV (p = 0.033), peak DF (p < 0.001), and PS number (p < 0.001) were more significant in PITX2 +/--deficient than wild-type AF. PITX2 +/--deficient AF was easier to terminate with class IC AADs than the wild-type AF (p = 0.018). Conclusions: The computational modeling-guided AAD test was feasible for evaluating the efficacy of multiple AADs in patients with AF. AF wave-dynamic and electrophysiological characteristics are different among the PITX2-deficient and the wild-type genotype models.ope

Ablation and antiarrhythmic drug effects on PITX2 +/- deficient atrial fibrillation: A computational modeling study

Introduction: Atrial fibrillation (AF) is a heritable disease, and the paired-like homeodomain transcription factor 2 (PITX2) gene is highly associated with AF. We explored the differences in the circumferential pulmonary vein isolation (CPVI), which is the cornerstone procedure for AF catheter ablation, additional high dominant frequency (DF) site ablation, and antiarrhythmic drug (AAD) effects according to the patient genotype (wild-type and PITX2 +/- deficient) using computational modeling. Methods: We included 25 patients with AF (68% men, 59.8 ± 9.8 years of age, 32% paroxysmal AF) who underwent AF catheter ablation to develop a realistic computational AF model. The ion currents for baseline AF and the amiodarone, dronedarone, and flecainide AADs according to the patient genotype (wild type and PITX2 +/- deficient) were defined by relevant publications. We tested the virtual CPVI (V-CPVI) with and without DF ablation (±DFA) and three virtual AADs (V-AADs, amiodarone, dronedarone, and flecainide) and evaluated the AF defragmentation rates (AF termination or changes to regular atrial tachycardia (AT), DF, and maximal slope of the action potential duration restitution curves (Smax), which indicates the vulnerability of wave-breaks. Results: At the baseline AF, mean DF (p = 0.003), and Smax (p < 0.001) were significantly lower in PITX2 +/- deficient patients than wild-type patients. In the overall AF episodes, V-CPVI (±DFA) resulted in a higher AF defragmentation relative to V-AADs (65 vs. 42%, p < 0.001) without changing the DF or Smax. Although a PITX2 +/- deficiency did not affect the AF defragmentation rate after the V-CPVI (±DFA), V-AADs had a higher AF defragmentation rate (p = 0.014), lower DF (p < 0.001), and lower Smax (p = 0.001) in PITX2 +/- deficient AF than in wild-type patients. In the clinical setting, the PITX2 +/- genetic risk score did not affect the AF ablation rhythm outcome (Log-rank p = 0.273). Conclusion: Consistent with previous clinical studies, the V-CPVI had effective anti-AF effects regardless of the PITX2 genotype, whereas V-AADs exhibited more significant defragmentation or wave-dynamic change in the PITX2 +/- deficient patients.ope

Association of ZFHX3 Genetic Polymorphisms and Extra-Pulmonary Vein Triggers in Patients With Atrial Fibrillation Who Underwent Catheter Ablation

Background: The ZFHX3 gene (16q22) is the second most highly associated gene with atrial fibrillation (AF) and is related to inflammation and fibrosis. We hypothesized that ZFHX3 is associated with extra-pulmonary vein (PV) triggers, left atrial (LA) structural remodeling, and poor rhythm outcomes of AF catheter ablation (AFCA). Methods: We included 1,782 patients who underwent a de novo AFCA (73.5% male, 59.4 ± 10.8 years old, 65.9% paroxysmal AF) and genome-wide association study and divided them into discovery (n = 891) and replication cohorts (n = 891). All included patients underwent isoproterenol provocation tests and LA voltage mapping. We analyzed the ZFHX3, extra-PV trigger-related factors, and rhythm outcomes. Result: Among 14 single-nucleotide polymorphisms (SNPs) of ZFHX3, rs13336412, rs61208973, rs2106259, rs12927436, and rs1858801 were associated with extra-PV triggers. In the overall patient group, extra-PV triggers were independently associated with the ZFHX3 polygenic risk score (PRS) (OR 1.65 [1.22-2.22], p = 0.001, model 1) and a low LA voltage (OR 0.74 [0.56-0.97], p = 0.029, model 2). During 49.9 ± 40.3 months of follow-up, clinical recurrence of AF was significantly higher in patients with extra-PV triggers (Log-rank p < 0.001, HR 1.89 [1.49-2.39], p < 0.001, model 1), large LA dimensions (Log-rank p < 0.001, HR 1.03 [1.01-1.05], p = 0.002, model 2), and low LA voltages (Log-rank p < 0.001, HR 0.73 [0.61-0.86], p < 0.001, model 2) but not the ZFHX3 PRS (Log-rank p = 0.819). Conclusion: The extra-PV triggers had significant associations with both ZFHX3 genetic polymorphisms and acquired LA remodeling. Although extra-PV triggers were an independent predictor of AF recurrence after AFCA, the studied AF risk SNPs intronic in ZFHX3 were not associated with AF recurrence.ope

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence

Objective: We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN). Methods: This study included 6358 subjects (872 cases, 5486 controls) from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling. Results: The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd-even registration numbers (AUC 0.51±0.01). Conclusions: AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs. Trial registration number: NCT02138695.ope

Effect of epicardial fat volume on outcomes after left atrial posterior wall isolation in addition to pulmonary vein isolation in patients with persistent atrial fibrillation

Background: Greater epicardial adipose tissue (EAT) is related to higher recurrences after atrial fibrillation catheter ablation (AFCA). We investigated the effects of posterior wall box isolation (POBI) in conjunction with circumferential pulmonary vein isolation (CPVI) on rhythm outcomes according to varying EAT volumes among patients with persistent atrial fibrillation (PeAF). Materials and methods: We included 1,187 patients with PeAF undergoing a de novo AFCA including those receiving CPVI alone (n = 687) and those receiving additional POBI (n = 500). The rhythm outcomes at 2 years post-AFCA were compared in subgroups stratified by the EAT volume using propensity overlap weighting. Results: A reduced EAT volume was linearly associated with more favorable rhythm outcomes for additional POBI than for CPVI alone (P for interaction = 0.002). Among the patients with smaller EAT volumes (≤116.23 mL, the median value, n = 594), additional POBI was associated with a reduced AF recurrence risk as compared to CPVI only [weighted HR (hazard ratio) 0.74, 95% CI (confidence interval) 0.56-0.99]. In contrast, among the remaining 593 patients with greater EAT volumes (>116.23 mL), No difference was observed in the recurrence risk between the additional POBI and CPVI alone groups (weighted HR 1.13, 95% CI 0.84-1.52). Among 205 patients with repeat ablations, the POBI reconnection rate was more frequent in the large EAT group (77.4%) than in the small EAT group (56.7%, P = 0.034). Conclusion: While PeAF patients with a smaller EAT volume averted AF recurrence by additional POBI after CPVI, no benefit of the POBI was observed in those with a greater EAT volume.ope

Anti-atrial Fibrillation Effects of Pulmonary Vein Isolation With or Without Ablation Gaps: A Computational Modeling Study

Background: Although pulmonary vein isolation (PVI) gaps contribute to recurrence after atrial fibrillation (AF) catheter ablation, the mechanism is unclear. We used realistic computational human AF modeling to explore the AF wave-dynamic changes of PVI with gaps (PVI-gaps). Methods: We included 40 patients (80% male, 61.0 ± 9.8 years old, 92.5% persistent AF) who underwent AF catheter ablation to develop our realistic computational AF model. We compared the effects of a complete PVI (CPVI) and PVI-gap (2-mm × 4) on the AF wave-dynamics by evaluating the dominant frequency (DF), spatial change of DF, maximal slope of the action potential duration restitution curve (Smax), and AF defragmentation rate (termination or change to atrial tachycardia), and tested the effects of additional virtual interventions and flecainide on ongoing AF with PVI-gaps. Results: Compared with the baseline AF, CPVIs significantly reduced extra-PV DFs (p < 0.001), but PVI-gaps did not. COV-DFs were greater after CPVIs than PVI-gaps (p < 0.001). Neither CPVIs nor PVI-gaps changed the mean Smax. CPVIs resulted in higher AF defragmentation rates (80%) than PVI-gaps (12.5%, p < 0.001). In ongoing AF after PVI-gaps, the AF defragmentation rates after a wave-breaking gap ablation, extra-PV DF ablation, or flecainide were 60.0, 34.3, and 25.7%, respectively (p = 0.010). Conclusion: CPVIs effectively reduced the DF, increased its spatial heterogeneity in extra-PV areas, and offered better anti-AF effects than extra-PV DF ablation or additional flecainide in PVI-gap conditions.ope

Clinical Usefulness of Virtual Ablation Guided Catheter Ablation of Atrial Fibrillation Targeting Restitution Parameter-Guided Catheter Ablation: CUVIA-REGAB Prospective Randomized Study

Background and objectives: We investigated whether extra-pulmonary vein (PV) ablation targeting a high maximal slope of the action potential duration restitution curve (Smax) improves the rhythm outcome of persistent atrial fibrillation (PeAF) ablation. Methods: In this open-label, multi-center, randomized, and controlled trial, 178 PeAF patients were randomized with 1:1 ratio to computational modeling-guided virtual Smax ablation (V-Smax) or empirical ablation (E-ABL) groups. Smax maps were generated by computational modeling based on atrial substrate maps acquired during clinical procedures in sinus rhythm. Smax maps were generated during the clinical PV isolation (PVI). The V-Smax group underwent an additional extra-PV ablation after PVI targeting the virtual high Smax sites. Results: After a mean follow-up period of 12.3±5.2 months, the clinical recurrence rates (25.6% vs. 23.9% in the V-Smax and the E-ABL group, p=0.880) or recurrence appearing as atrial tachycardia (11.1% vs. 5.7%, p=0.169) did not differ between the 2 groups. The post-ablation cardioversion rate was higher in the V-Smax group than E-ABL group (14.4% vs. 5.7%, p=0.027). Among antiarrhythmic drug-free patients (n=129), the AF freedom rate was 78.7% in the V-Smax group and 80.9% in the E-ABL group (p=0.776). The total procedure time was longer in the V-Smax group (p=0.008), but no significant difference was found in the major complication rates (p=0.497) between the groups. Conclusions: Unlike a dominant frequency ablation, the computational modeling-guided V-Smax ablation did not improve the rhythm outcome of the PeAF ablation and had a longer procedure time. Trial registration: ClinicalTrials.gov Identifier: NCT02558699.ope