多模式数据集成预测心房动
Yuchen Yao1,2, Michael J Zhang3,4, Wendy Wang5
1School of Statistics, College of Liberal Arts, University of Minnesota, 313 Church Street SE, Minneapolis, MN 55455, USA.
European heart journal. Digital health
|January 23, 2025
概括
结合临床和多基因风险得分,可以有效预测心房动 (AF). 添加心电图或蛋白质数据只为AF风险预测提供了微小的改善.
科学领域:
- 心血管疾病的研究研究.
- 发现生物标志物的发现.
- 在医疗保健中的预测分析.
背景情况:
- 预测心房动 (AF) 风险通常使用临床变量,多基因风险得分,心电图和血蛋白.
- 在单一研究中综合这些不同数据源的全面整合是有限的.
研究的目的:
- 评估临床变量,多基因风险得分,心电图和心房的血蛋白质的联合预测能力.
- 确定AF风险预测的最有效和最节的方法.
主要方法:
- 利用了来自8374名 (第三次访问) 和3730名 (第五次访问) 社区动脉样硬化风险研究参与者的数据.
- 构建的临床,多基因,蛋白质和心电图风险得分.
- 使用后勤回归和曲线下的面积 (AUC) 评估预测性能.
主要成果:
- 将多基因风险得分添加到临床变量中,使发生性AF的AUC从0.660提高到0.752,以及流行性AF的AUC从0.737提高到0.854.
- 进一步纳入心电图和蛋白质风险评分,导致AUC增加至0.763 (事件) 和0.875 (流行).
结论:
- 临床和多基因风险评分的组合提供了最有效和节的AF预测方法.
- 电脑电图和蛋白质风险评分除了临床和多基因评分之外,提供了有限的额外预测价值.
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