Emanuel Heil, Nikolaos Dagres, Leif-Hendrick Boldt, Abdul Parwani, Florian Blaschke, Doreen Schoeppenthau, Philipp Attanasio, Robert Hättasch, Verena Tscholl, Gerhard Hindricks, Jin-Hong Gerds-Li, Felix Hohendanner
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引用次数: 0
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: AI-guided stD mapping effectively identifies atrial remodeling beyond classical voltage-derived substrate, supporting its potential as a useful adjunctive tool in AF characterization.
背景：人工智能（AI）引导的时空弥散（stD）映射已被证明可以改善持续性心房颤动（AF）患者的预后。然而，性病定位与心房心肌病标志物之间的关系、阵发性与持续性房颤的弥散模式、弥散区域的稳定性以及性病引导的消融相关结果在所有角落队列中仍然难以捉摸。方法：在这项回顾性单中心分析中，房颤患者使用VOLTA AF Explorer软件进行高密度电解剖定位，同时进行多例stD定位。肺静脉隔离（PVI）和左房离散区靶向消融。收集临床、超声心动图、生物标志物和低压区（LVA）数据作为左房重构的标志物。结果：在92%的患者中发现了性病分散。平均诊断时间为7±1年。总体而言，58%的患者显示弥散只与低压区共定位，而42%的患者弥散延伸到中电压或正常电压区。弥散负荷与LVA程度和其他重塑标志物如NT-proBNP和LAVI密切相关。与阵发性房颤相比，持续性房颤患者弥散位点的数量明显增加。在持续性房颤复律前后，弥散模式基本保持一致，左后心房壁成为常见的热点。在随访中，33%的阵发性房颤患者和60%的持续性房颤患者在仅限于左心房的弥散消融中复发。尽管有这些复发，大多数患者报告症状负担有所改善。结论：人工智能引导的stD映射有效地识别了经典电压衍生基底之外的心房重构，支持其作为房颤表征有用的辅助工具的潜力。
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