Park Hanjin, Kwon Oh-Seok, Shim Jaemin, Kim Daehoon, Park Je-Wook, Kim Yun-Gi, Yu Hee Tae, Kim Tae-Hoon, Uhm Jae-Sun, Choi Jong-Il, Joung Boyoung, Lee Moon-Hyoung, Pak Hui-Nam
Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea.
Division of Cardiology, Department of Internal Medicine, Korea University Medical Center, Seoul, Republic of Korea.
NPJ Digit Med. 2024 Sep 5;7(1):234. doi: 10.1038/s41746-024-01234-1.
The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.
将人工智能（AI）算法应用于12导联心电图（ECG）可提供前景广阔的年龄预测模型。我们探讨了术前AI-ECG年龄与实际年龄之间的差距是否能够预测导管消融术后房颤（AF）的复发情况。我们在四个跨国数据集中验证了一个基于预训练残差网络的年龄预测模型。然后，我们在两个独立的房颤消融队列中，对接受初发性房颤导管消融且正在服用抗心律失常药物的个体，使用术前窦性心律心电图来估算AI-ECG年龄。我们根据从四个模型验证数据集获得的AI-ECG年龄差距的平均绝对误差，将AI-ECG年龄差距进行分类；分为年龄-ECG（≥10岁）组和正常ECG年龄（<10岁）组。在这两个房颤消融队列中，与正常ECG年龄组相比，年龄-ECG组房颤复发风险显著增加。这些关联独立于实际年龄或左心房直径。总之，术前AI-ECG年龄对导管消融术后房颤复发具有预后价值。
