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使用机器学习模型来预测PAD中复血管化后血栓的发生.

Samir Ghandour1, Adriana A Rodriguez Alvarez1, Isabella F Cieri1

  • 1Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA, United States.

Frontiers in artificial intelligence
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概括

在下肢再血管化后预测动脉血栓事件至关重要. 结合患者数据和粘弹性测试的机器学习模型有效地识别了这些事件的高风险患者.

关键词:
机器学习是机器学习.预后 预后 预后重新血管化的过程.用血小板映射绘制的血小板显微镜.血栓形成的原因是血栓形成.

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科学领域:

  • 血管外科 血管外科
  • 心血管医学 心血管医学
  • 生物医学工程 生物医学工程

背景情况:

  • 下肢再血管化 (LER) 后的移植/支架血栓是周围动脉疾病 (PAD) 患者的严重并发症,可能导致截肢.
  • 在LER后一年内预测动脉血栓事件 (ATE) 对患者的结果至关重要.
  • 高血栓形成率需要新的预测策略LER程序.

研究的目的:

  • 开发一种机器学习模型 (MLM) 来预测LER之后的ATE.
  • 整合粘弹性测试 (血小板测绘的血小板结晶造影 - - TEG-PM) 和患者特异性变量到MLM.
  • 改进对LER后ATE高风险患者的鉴定.

主要方法:

  • 接受LER (2020-2024) 的PAD患者的潜在招募.
  • 收集人口,临床,干预和外科手术期间的TEG-PM数据.
  • 开发和评估使用SMOTE进行类不平衡和交叉验证的MLM (后勤回归,XGBoost,决策树).

主要成果:

  • 在308名患者中,18.3%的患者在LER后一年内经历了ATE.
  • 整合TEG-PM和基线特征的物流回归MLM实现了0.76.7的AUC.
  • 表现最好的MLM显示了70%的准确性,68%的敏感性和71%的特异性.

结论:

  • 在MLM中将患者特征与TEG-PM值结合起来,可以有效地预测LER后的ATE.
  • 这种方法提高了高风险PAD患者的鉴定.
  • 根据这些预测,可以制定定制的血栓预防策略.