数字双胞胎整合了临床,形态和血液动力学数据,以确定中风风险因素
Marta Saiz-Vivó1, Jordi Mill2, Xavier Iriart3,4
1Physense, BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain. marta.saiz@upf.edu.
NPJ digital medicine
|June 17, 2025
概括
数字双胞胎模型整合了心房患者数据,改善了中风风险分层. 结合成像和临床因素,确定了不同的患者群体,增强了超越传统得分的血栓风险评估.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 缺血性中风是死亡的主要原因,通常与心房动 (AF) 有关.
- 目前的中风风险评估 (例如,CHA2DS2-VASc) 主要使用临床数据,忽视左心室 (LA) 和左心室附属体 (LAA) 内的关键解剖和流动动力学.
- 有限的研究已经将详细的LA形态和血液动力学与临床数据进行了综合性风险评估.
研究的目的:
- 开发和验证数字双胞胎框架,以提高心房动患者血栓风险分层.
- 整合LA形态学,血液动力学和临床数据,以改善患者的表型.
- 根据综合数据,根据综合数据确定具有不同中风风险的患者子组.
主要方法:
- 开发了一个数字双胞胎框架,集成统计和机械模型.
- 无监督多核学习被应用到来自130名AF患者的数据中.
- 该模型结合了左心房 (LA) 形态,血液动力学和临床数据,包括B型尿素水平.
主要成果:
- 综合方法成功地将患者分为三个不同的现象群.
- 风险最高的组具有较大的心房尺寸,复杂的LAA解剖学和升高的B型尿素的特征.
- 这种现象组表现出更高的血栓形成倾向.
结论:
- 数字双胞胎模型为评估AF患者血栓形成风险提供了强大的工具.
- 与单独的临床分数相比,整合形态和血液动力学数据显著改善了患者分层.
- 需要进一步的研究来完善这些模型以准确预测中风.
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