一种基于机器学习的方法来识别动脉亚临床动脉样硬化的内型
Qiao Sen Chen1, Otto Bergman1, Louise Ziegler2
1Division of Cardiovascular Medicine, Department of Medicine Solna, Karolinska Institutet, Solnavägen 30, 171 64 Stockholm, Sweden.
Cardiovascular research
|July 21, 2023
概括
研究人员确定了四种不同的冠状动脉样硬化末型,区分风险概况从轻度到重度. 这种精准医学方法改善了动脉样硬化心血管疾病 (ASCVD) 风险预测和预防策略.
科学领域:
- 心血管医学 心血管医学
- 生物标志物 生物标志物
- 医疗成像医学成像
背景情况:
- 动脉中的亚临床动脉样硬化是未来心血管事件的关键指标.
- 鉴定出动脉样硬化的不同生物亚型 (内型) 可以完善风险预测.
- 目前的风险评估模型可能无法完全捕捉动脉样硬化进展的异质性.
研究的目的:
- 定义和表征动脉亚临床动脉样硬化的不同内型.
- 调查这些内型与动脉样硬化进展和动脉样硬化心血管疾病 (ASCVD) 风险的关联.
- 评估基于内型的模型对改善ASCVD风险预测的有用性.
主要方法:
- 在IMPROVE队列 (n=3340) 中,综合人口,临床和分子数据与带超声波.
- 应用神经网络和层次聚类来识别内型,使用SHapley添加式扩展 (SHAP) 进行特征分析.
- 在独立的PIVUS队列 (n=1061) 中验证的内型预测.
主要成果:
- 鉴定了四种不同的动脉样硬化内型,从轻度 (内型1) 到严重 (内型4) 之间.
- 与1型相比,4型内分泌体显示出明显更厚的喉内膜-介质厚度 (c-IMT),更多的斑块,更快的进展和更高的ASCVD风险.
- 基于内型的模型在两个队列中始终改善了ASCVD风险歧视和重新分类.
结论:
- 确定了四种次临床动脉动脉样硬化的可复制的内型,与疾病进展和ASCVD风险有关.
- 基于内型的方法为准确医学在ASCVD预防方面提供了一个有前途的战略.
- 这些发现可以指导针对不同动脉样硬化风险水平的个体进行有针对性的干预.
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