用心电图预测认知障碍，准确率超86%，且结果可解释。
Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms
- 基于心电图特征与人口统计学数据构建机器学习模型
- 阿尔茨海默病预测外部验证AUROC达0.863，痴呆达0.865
- 模型可解释性强，适合临床早期筛查与个性化监测
背景：心电图（ECG）分析已成为检测非心脏疾病相关生理变化的有前景工具。鉴于心血管与神经认知健康间的紧密关联，患有共病神经认知障碍的个体可能出现心电图异常。这凸显了心电图作为生物标志物在神经认知障碍的检测、治疗监测和风险分层中的潜力，但该领域仍待深入探索。方法：我们旨在验证从心电图特征预测神经认知障碍的可行性，覆盖多种患者群体。利用心电图特征和人口统计学数据，预测基于ICD-10编码定义的神经认知障碍，包括痴呆、谵妄和帕金森病。在MIMIC-IV和ECG-View数据集上进行了内部与外部验证。通过AUROC评估预测性能，并使用Shapley值解释特征贡献。结果：在神经认知障碍中表现出显著预测性能。其中，F03：痴呆的预测性能最高，内部AUROC为0.848（95% CI: 0.848–0.848），外部AUROC为0.865（0.864–0.965）；其次为G30：阿尔茨海默病，内部AUROC为0.809（95% CI: 0.808–0.810），外部AUROC为0.863（95% CI: 0.863–0.864）。特征重要性分析揭示了已知与新发现的心电图相关指标。心电图作为非侵入性、可解释的生物标志物，在特定神经认知障碍中具有应用前景。本研究展示了跨队列的稳健性能，为未来临床应用（如早期检测与个性化监测）奠定了基础。
原文摘要 · Abstract (English)
Background: Electrocardiogram (ECG) analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders. Given the close connection between cardiovascular and neurocognitive health, ECG abnormalities may be present in individuals with co-occurring neurocognitive conditions. This highlights the potential of ECG as a biomarker to improve detection, therapy monitoring, and risk stratification in patients with neurocognitive disorders, an area that remains underexplored. Methods: We aim to demonstrate the feasibility to predict neurocognitive disorders from ECG features across diverse patient populations. We utilized ECG features and demographic data to predict neurocognitive disorders defined by ICD-10 codes, focusing on dementia, delirium, and Parkinson's disease. Internal and external validations were performed using the MIMIC-IV and ECG-View datasets. Predictive performance was assessed using AUROC scores, and Shapley values were used to interpret feature contributions. Results: Significant predictive performance was observed for disorders within the neurcognitive disorders. Significantly, the disorders with the highest predictive performance is F03: Dementia, with an internal AUROC of 0.848 (95% CI: 0.848-0.848) and an external AUROC of 0.865 (0.864-0.965), followed by G30: Alzheimer's, with an internal AUROC of 0.809 (95% CI: 0.808-0.810) and an external AUROC of 0.863 (95% CI: 0.863-0.864). Feature importance analysis revealed both known and novel ECG correlates. ECGs hold promise as non-invasive, explainable biomarkers for selected neurocognitive disorders. This study demonstrates robust performance across cohorts and lays the groundwork for future clinical applications, including early detection and personalized monitoring.
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