在一项纵向人口研究中,可解释的AI将心电图衰老效应与心血管风险增加联系起来
Philip Hempel1,2, Antônio H Ribeiro3, Marcus Vollmer4,5
1Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany. philip.hempel@med.uni-goettingen.de.
NPJ digital medicine
|January 13, 2025
概括
人工智能-心电图 (AI-ECG) 模型可以利用纵向心电图 (ECG) 的衰老效应预测心血管疾病风险. 这种方法增强了早期患者风险识别,并改善了心血管健康结果.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 生物标志物 生物标志物
背景情况:
- 衰老会影响12导电心电图 (ECG),与心血管疾病 (CVD) 相对应.
- AI-ECG模型估计衰老效应作为一种新的生物标志物,但通常使用单个ECG,忽视纵向数据.
- 在不同人群和纵向数据上验证AI-ECG模型对于临床应用至关重要.
研究的目的:
- 为了验证在巴西数据上训练的AI-ECG模型,使用超过20年后续的德国队列来验证.
- 评估AI-ECG模型中纵向心电图数据对心血管风险预测的有用性.
- 研究AI-ECG衍生的衰老效应与特定的心血管结果和死亡率的关联.
主要方法:
- 在使用长度心电图数据 (超过20年) 的德国队列上验证预训练的AI-ECG模型.
- 使用单个心电图与纵向心电图的模型性能比较.
- 应用可解释AI (XAI) 方法来解释模型预测.
- 对心血管事件和死亡率的危险比率和几率比率的统计分析.
主要成果:
- 与最初的巴西研究 (r2 = 0.71) 相比,AI-ECG模型在德国队列中显示了类似的性能 (r2 = 0.70).
- 整合纵向心电图数据显著加强了与心血管风险的关联,使死亡风险比率从1.43增加到1.65.
- 由AI-ECG衍生的衰老效应与心房动,心力衰竭和死亡率的增加显著相关.
- 可解释的AI证实,该模型专注于与衰老相关的临床相关的ECG特征.
结论:
- 对衰老影响的长度AI-ECG分析是评估心血管风险的可靠方法.
- 这种方法可以作为一种新的人口水平生物标志物,用于早期识别有风险的个体.
- 经过验证的AI-ECG模型显示了改善心血管疾病的早期检测和管理的前景.
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