PDF(Pubmed)
Abstract：
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
摘要：
房颤（AF）伴多种并发症，高发病率和死亡率，治愈率低，已经成为全球性的公共卫生问题。尽管以抗AF药物和射频消融为代表的治疗方法取得了重大进展，治疗效果不如预期。其原因主要是由于我们对房颤机制的认识不足。该领域受益于机械和（或）统计方法。最近通过机械和统计模型之间的协同作用对数字孪生技术重新产生了兴趣，为AF分析开辟了新的领域。在审查中,我们简要介绍了引起AF病理生理学和当前治疗方式的发现。然后，我们从三个方面总结了数字孪生技术的成就：理解AF机制，筛选抗房颤药物并优化消融策略。最后,我们讨论了阻碍数字孪生心脏临床应用的挑战。随着数据重用和共享的快速发展，我们希望它们的应用能够实现从AF描述到响应预测的过渡。
