Department of Cardiology, Seoul National University Bundang Hospital, Gyeonggi-do.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul.
J Cardiovasc Med (Hagerstown). 2024 Nov 1;25(11):781-788. doi: 10.2459/JCM.0000000000001670. Epub 2024 Sep 12.
Cardiovascular risk assessment is a critical component of healthcare, guiding preventive and therapeutic strategies. In this study, we developed and evaluated an image-based electrocardiogram (ECG) analyzing an artificial intelligence (AI) model that estimates biological age and mortality risk.
Using a dataset of 978 319 ECGs from 250 145 patients at Seoul National University Bundang Hospital, we developed a deep-learning model utilizing printed 12-lead ECG images to estimate patients' age (ECG-Age) and 1- and 5-year mortality risks. The model was validated externally using the CODE-15% dataset from Brazil.
The ECG-Age showed a high correlation with chronological age in both the internal and external validation datasets (Pearson's R = 0.888 and 0.852, respectively). In the internal validation, the direct mortality risk prediction models showed area under the curves (AUCs) of 0.843 and 0.867 for 5- and 1-year all-cause mortality, respectively. For 5- and 1-year cardiovascular mortality, the AUCs were 0.920 and 0.916, respectively. In the CODE-15%, the mortality risk predictions showed AUCs of 0.818 and 0.836 for the prediction of 5- and 1-year all-cause mortality, respectively. Compared to the neutral Delta-Age (ECG-Age - chronological age) group, hazard ratios for deaths were 1.88 [95% confidence interval (CI): 1.14-3.92], 2.12 (95% CI: 1.15-3.92), 4.46 (95% CI: 2.22-8.96) and 7.68 (95% CI: 3.32-17.76) for positive Delta-Age groups (5-10, 10-15, 15-20, >20), respectively.
An image-based AI-ECG model is a feasible tool for estimating biological age and assessing all-cause and cardiovascular mortality risks, providing a practical approach for utilizing standardized ECG images in predicting long-term health outcomes.
心血管风险评估是医疗保健的一个关键组成部分,指导着预防和治疗策略。在这项研究中,我们开发并评估了一种基于图像的心电图(ECG)分析人工智能(AI)模型,该模型可估计生物年龄和死亡风险。
我们使用来自首尔国立大学盆唐医院的 250145 名患者的 978319 份 12 导联心电图数据集,开发了一种利用打印的 12 导联心电图图像来估计患者年龄(ECG-Age)和 1 年和 5 年死亡风险的深度学习模型。该模型在巴西的 CODE-15%数据集上进行了外部验证。
ECG-Age 在内部和外部验证数据集中与实际年龄高度相关(Pearson 的 R 分别为 0.888 和 0.852)。在内部验证中,直接死亡率预测模型的 5 年和 1 年全因死亡率的曲线下面积(AUC)分别为 0.843 和 0.867。对于 5 年和 1 年心血管死亡率,AUC 分别为 0.920 和 0.916。在 CODE-15%中,死亡风险预测的 5 年和 1 年全因死亡率的 AUC 分别为 0.818 和 0.836。与中性 Delta-Age(ECG-Age-实际年龄)组相比,死亡的危险比分别为 1.88(95%置信区间[CI]:1.14-3.92)、2.12(95%CI:1.15-3.92)、4.46(95%CI:2.22-8.96)和 7.68(95%CI:3.32-17.76)。
基于图像的 AI-ECG 模型是一种可行的工具,可用于估计生物年龄和评估全因和心血管死亡率风险,为利用标准化 ECG 图像预测长期健康结果提供了一种实用方法。