在联合模型中使用纵向样本缩来动态预测心房的发作
Nicolas Ngo1, Aline Campos Reis de Souza2, Roch Giorgi3
1Aix Marseille Univ, Inserm, IRD, SESSTIM, ISSPAM, Marseille, France. nicolas.ngo@univ-amu.fr.
BMC medical research methodology
|February 9, 2026
概括
这项研究引入了一种联合建模方法,使用样本 (SampEn) 和基线数据在12小时内预测心房 (AF) 发作. 这些模型提供了动态的,个性化的风险估计,以更好地监测患者.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 心房动 (AF) 是一种常见的心律失常,增加了中风风险.
- 早期的AF预测可以提高患者的护理.
- 样本 (SampEn) 是信号复杂性的度量,显示出AF预测的潜力.
研究的目的:
- 开发和评估用于短期AF风险预测的联合建模方法.
- 为了结合基线共变量和纵向SampEn轨迹.
- 为了估计未来12小时内AF发作的风险.
主要方法:
- 开发了几个具有不同纵向过程复杂性的联合模型.
- 在IRIDIA-AF数据集上使用引导复制来评估模型性能.
- 使用时间依赖的AUC,灵敏度,特异性,校准和准确度指标评估预测性能.
主要成果:
- 最好的模型,使用自然立方线,达到64.4%的AUC和77.63%的灵敏度.
- 一个更简单的线性模型产生了最高的特异性 (77.94%).
- 针对被选中的患者,创建了个别预测资料.
结论:
- 联合模型对短期AF风险预测有希望.
- 这些模型提供了动态和个性化的风险估计.
- 随着新数据的出现,启用更新的预测和个性化监控.
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