Background and Aims Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS). Methods Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set. Results A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of .78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02–4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC = .62) and PGS (AUC-ROC = .59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P < .001), with minimal changes to the AUC-ROC (.76–.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC = .77). Conclusions ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.

中文翻译：

---

使用深度学习、临床模型和多基因评分预测新发心房颤动


背景和目标应用于心电图 （ECG-AI） 的深度学习是一种预测心房颤动或扑动 （AF） 的新兴方法。本研究介绍了在三级心脏中心开发和测试的 ECG-AI 模型，将其性能与临床模型和 AF 多基因评分 （PGS） 进行比较。方法 分析蒙特利尔心脏研究所的窦性心律心电图，不包括既往 AF 患者的心电图。主要结局是 5 年时 AF 事件。通过将患者分成不重叠的数据集来开发 ECG-AI 模型：70% 用于训练，10% 用于验证，20% 用于测试。在测试数据集中评估了 ECG-AI 、临床模型和 PGS 的性能。ECG-AI 模型在重症监护医学信息市场 IV （MIMIC-IV） 医院数据集中进行了外部验证。结果 共纳入 145 323 例患者的 669 782 例心电图。平均年龄为 61 ± 15 岁，其中 58% 为男性。在 15% 的患者中观察到主要结果，ECG-AI 模型显示受试者工作特征下面积 （AUC-ROC） 曲线为 .78。在包括第一次心电图在内的事件发生时间分析中，ECG-AI 对高风险的推断确定了 26% 的人群发生 AF 的风险增加了 4.3 倍（95% 置信区间：4.02-4.57）。在对 2301 名患者的亚组分析中，ECG-AI 优于 CHARGE-AF （AUC-ROC = .62） 和 PGS （AUC-ROC = .59）。在 ECG-AI 中添加 PGS 和 CHARGE-AF 提高了拟合优度 （似然比检验 P < .001），而 AUC-ROC 的变化最小 （.76–.77）。在外部验证队列 （平均年龄 59 ± 18 岁，47% 为男性，中位随访 1.1 年）中，ECG-AI 模型性能保持一致 （AUC-ROC = .77）。 结论 ECG-AI 为预测三级心脏中心新发 AF 提供了一种准确的工具，超过了临床和 PGS。