Vlachakis Panayotis K, Theofilis Panagiotis, Apostolos Anastasios, Karakasis Paschalis, Ktenopoulos Nikolaos, Boulmpou Aristi, Drakopoulou Maria, Leontsinis Ioannis, Xydis Panagiotis, Kordalis Athanasios, Koniari Ioanna, Gatzoulis Konstantinos A, Sideris Skevos, Tsioufis Costas
1st Department of Cardiology, "Hippokration" General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Second Department of Cardiology, Aristotle University of Thessaloniki, Hippokration General Hospital, 54124 Thessaloniki, Greece.
J Clin Med. 2025 Apr 23;14(9):2919. doi: 10.3390/jcm14092919.
Atrial fibrillation (Afib) recurrence after catheter ablation (CA) remains a significant clinical challenge, driven by a complex and dynamic interplay of structural, electrical, and autonomic mechanisms. While pulmonary vein isolation (PVI) is the cornerstone of CA, recurrence rates remain substantial, highlighting the need to understand the evolving pathophysiology beyond PV reconnection. Post-ablation changes, including inflammation, edema, oxidative stress, and ischemia, create a transient proarrhythmic state that may contribute to early recurrence. Over time, atrial remodeling, fibrosis, and residual autonomic activity further sustain arrhythmogenicity. Additionally, epicardial adipose tissue promotes atrial myopathy, accelerating disease progression, particularly in patients with risk factors such as older age, female sex, obesity, hypertension, obstructive sleep apnea, and heart failure. The multifactorial nature of Afib recurrence underscores the limitations of a "one-size-fits-all" ablation strategy. Instead, a patient-specific approach integrating advanced mapping techniques, multimodal imaging, and computational modeling is essential. Artificial intelligence (AI) and digital twin models hold promise for predicting recurrence by simulating individualized disease progression and optimizing ablation strategies. However, challenges remain regarding the standardization and validation of these novel approaches. A deeper understanding of the dynamic interconnections between the mechanisms driving recurrence is crucial for improving long-term CA outcomes. This review explores the evolving nature of Afib recurrence, emphasizing the need for a precision medicine approach that accounts for the continuous interaction of pathophysiological processes in order to refine patient selection, ablation strategies, and post-procedural management.
导管消融（CA）术后房颤（Afib）复发仍然是一项重大的临床挑战，其由结构、电和自主神经机制的复杂动态相互作用所驱动。虽然肺静脉隔离（PVI）是CA的基石，但复发率仍然很高，这凸显了理解肺静脉重新连接之外不断演变的病理生理学的必要性。消融后变化，包括炎症、水肿、氧化应激和缺血，会产生一种短暂的促心律失常状态，这可能导致早期复发。随着时间的推移，心房重塑、纤维化和残留的自主神经活动会进一步维持心律失常的发生。此外，心外膜脂肪组织会促进心房肌病，加速疾病进展，尤其是在老年、女性、肥胖、高血压、阻塞性睡眠呼吸暂停和心力衰竭等有危险因素的患者中。Afib复发的多因素性质凸显了“一刀切”消融策略的局限性。相反，采用整合先进标测技术、多模态成像和计算模型的个体化方法至关重要。人工智能（AI）和数字孪生模型有望通过模拟个体化疾病进展和优化消融策略来预测复发。然而，这些新方法的标准化和验证仍然存在挑战。深入了解驱动复发的机制之间的动态联系对于改善CA的长期疗效至关重要。本综述探讨了Afib复发的演变性质，强调需要一种精准医学方法，该方法考虑到病理生理过程的持续相互作用，以便优化患者选择、消融策略和术后管理。
