Heil Emanuel, Dagres Nikolaos, Boldt Leif-Hendrick, Parwani Abdul, Blaschke Florian, Schoeppenthau Doreen, Attanasio Philipp, Hättasch Robert, Tscholl Verena, Hindricks Gerhard, Gerds-Li Jin-Hong, Hohendanner Felix
Deutsches Herzzentrum Der Charité (DHZC), Charitéplatz 1, 10117, Berlin, Germany.
DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.
J Interv Card Electrophysiol. 2025 Jun 6. doi: 10.1007/s10840-025-02083-y.
Artificial intelligence (AI)-guided spatiotemporal dispersion (stD) mapping has been shown to improve outcomes in patients with persistent atrial fibrillation (AF). However, the relationship between stD mapping and markers of atrial cardiomyopathy, dispersion patterns in paroxysmal versus persistent AF, stability of dispersion regions, and stD-guided ablation-related outcomes in all-comer cohorts remain elusive.
In this retrospective single-center analysis, AF patients underwent high-density electroanatomical mapping alongside multiple instances of stD mapping using VOLTA AF Explorer software. Pulmonary vein isolation (PVI) and targeted ablation of left atrial dispersion regions were performed. Clinical, echocardiographic, biomarker, and low-voltage area (LVA) data were collected as markers of left atrial remodeling.
stD mapping identified dispersion in 92% of patients. Mean time since AF diagnosis was 7 ± 1 years. Overall, 58% of patients showed dispersion exclusively co-localizing with low-voltage areas, while 42% had dispersion extending into intermediate or normal voltage regions. Dispersion burden correlated strongly with LVA extent and other remodeling markers such as NT-proBNP and LAVI. Persistent AF patients exhibited a significantly higher number of dispersion sites compared to paroxysmal AF. Dispersion patterns remained largely consistent before and after cardioversion in persistent AF, with the posterior left atrial wall emerging as a common hotspot. At follow-up, AF recurred in 33% of paroxysmal and 60% of persistent AF patients who had dispersion ablation limited to the left atrium. Despite these recurrences, most patients reported an improvement in symptomatic burden.
AI-guided stD mapping effectively identifies atrial remodeling beyond classical voltage-derived substrate, supporting its potential as a useful adjunctive tool in AF characterization.
人工智能(AI)引导的时空离散度(stD)映射已被证明可改善持续性心房颤动(AF)患者的治疗效果。然而,在所有患者队列中,stD映射与心房心肌病标志物之间的关系、阵发性与持续性AF的离散模式、离散区域的稳定性以及stD引导的消融相关结果仍不明确。
在这项回顾性单中心分析中,AF患者使用VOLTA AF Explorer软件进行了高密度电解剖标测以及多次stD标测。进行了肺静脉隔离(PVI)和左心房离散区域的靶向消融。收集临床、超声心动图、生物标志物和低电压区(LVA)数据作为左心房重构的标志物。
stD映射在92%的患者中识别出离散度。自AF诊断以来的平均时间为7±1年。总体而言,58%的患者显示离散度仅与低电压区共定位,而42%的患者离散度延伸至中等或正常电压区域。离散度负荷与LVA范围以及其他重构标志物如NT-proBNP和LAVI密切相关。与阵发性AF患者相比,持续性AF患者的离散位点数量显著更高。持续性AF患者在复律前后的离散模式基本保持一致,左心房后壁成为一个常见的热点区域。在随访中,仅在左心房进行离散度消融的阵发性AF患者中有33%复发,持续性AF患者中有60%复发。尽管有这些复发情况,但大多数患者报告症状负担有所改善。
AI引导的stD映射有效地识别了超出经典电压衍生基质的心房重构,支持其作为AF特征描述中有用辅助工具的潜力。