房颤射频消融术后预后预测指标的研究进展

Progress in Prognostic Predictors Following Radiofrequency Ablation for Atrial Fibrillation: A Review of Recent Evidence

摘要: 射频导管消融(Radiofrequency Catheter Ablation, RFCA)已成为房颤(Atrial Fibrillation, AF)节律控制的重要治疗策略，但术后复发率仍维持在30%~50%。准确识别高危复发患者对于优化患者选择、制定个体化随访策略具有重要意义。近五年来，随着精准医学和人工智能技术的发展，房颤消融术后预后预测指标研究取得了显著进展。本文综述了2020~2025年间该领域的主要研究成果，将预测指标归纳为四大类：1) 临床及人口学指标，包括CHA₂DS₂-VASc评分、房颤类型、早期复发、消融策略及生活方式管理等；2) 影像学指标，涵盖左房结构、功能参数、心脏磁共振延迟钆强化(LGE)、左房应变及CT影像组学；3) 血清生物标志物，包括利钠肽类、炎症标志物、心肌纤维化标志物及代谢相关指标；4) 新兴技术：机器学习与人工智能模型。现有证据表明，多指标联合预测模型显著优于单一指标，而融合临床、影像及生物标志物的人工智能模型展现出最高的预测准确性。未来研究应致力于建立标准化、可解释且经过外部验证的预测体系，以实现房颤消融治疗的真正个体化。

Abstract: Radiofrequency catheter ablation (RFCA) has become a critical therapeutic strategy for rhythm control in atrial fibrillation (AF), yet the postoperative recurrence rate remains at 30%~50%. Accurate identification of high-risk patients for recurrence is essential for optimizing patient selection and developing individualized follow-up strategies. Over the past five years, significant progress has been made in prognostic predictor research for AF ablation following advancements in precision medicine and artificial intelligence technologies. This review summarizes major research achievements in this field from 2020 to 2025, categorizing predictors into four major groups: 1) Clinical and demographic indicators, including CHA₂DS₂-VASc score, AF type, early recurrence, ablation strategy, and lifestyle management; 2) Imaging indicators, encompassing left atrial structure and functional parameters, cardiac magnetic resonance delayed gadolinium enhancement (LGE), left atrial strain, and CT imaging omics; 3) Serum biomarkers, such as natriuretic peptides, inflammatory markers, myocardial fibrosis markers, and metabolic-related indicators; 4) Emerging technologies: machine learning and artificial intelligence models. Current evidence demonstrates that multi-index combined predictive models significantly outperform single indicators, with AI models integrating clinical, imaging, and biomarker data exhibiting the highest predictive accuracy. Future research should focus on establishing standardized, interpretable, and externally validated predictive systems to achieve true individualization in AF ablation therapy.