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机器学习预测了直接口服抗凝剂的心房患者的出血风险.

Rahul Chaudhary1, Mehdi Nourelahi2, Floyd W Thoma3

  • 1Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Department of Computer Science, Georgia Institute of Technology, Atlanta, Georgia; AI-HEART Lab, Pittsburgh, Pennsylvania.

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概括

机器学习模型显著提高了对直接口服抗凝剂 (DOACs) 的非膜心肌动 (AF) 患者的主要出血事件的预测,超过了传统的风险评分,并确定了个性化护理的新风险因素.

关键词:
心房动是心房动的一种.直接口服抗凝剂 直接口服抗凝剂血流性中风 血流性中风机器学习是机器学习.主要出血 严重出血风险预测风险预测

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科学领域:

  • 心脏病学 心脏病学
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 在接受直接口服抗凝剂 (DOACs) 的非膜动 (AF) 患者中预测严重出血对于量身定制治疗至关重要.
  • 现有的风险评分如HAS-BLED,ATRIA和ORBIT在准确评估出血风险方面存在局限性.
  • 左心房尾关闭是一种减少中风风险的替代方案,减少非手术性出血.

研究的目的:

  • 将各种机器学习 (ML) 模型的性能与常规出血风险得分 (HAS-BLED,ATRIA,ORBIT) 进行比较,以预测在DOAC上AF患者的出血事件.
  • 评估ML模型在多个时间点 (1,2,5年) 由于出血事件而导致住院的预测准确度.

主要方法:

  • 在匹兹堡大学医学中心利用2010-2022年电子健康记录进行了回顾性队列研究.
  • 包括24,468名非膜性AF患者 (年龄≥18岁) 在DOAC中,不包括那些先前有显著出血或华法林使用的患者.
  • 将ML算法 (后勤回归,分类树,随机森林,XGBoost,k-最近邻居,naive Bayes) 与HAS-BLED,ATRIA和ORBIT进行比较,用于预测出血住院.

主要成果:

  • 与HAS-BLED,ATRIA和ORBIT相比,ML模型在预测1年出血事件方面表现优越.
  • 随机森林模型实现了0.76的AUC,明显超过了HAS-BLED的AUC0.57 (p < 0.001).
  • 在所有随访时间点和预测出血性中风方面,ML模型的准确性有所提高,SHAP分析揭示了新的风险因素,如BMI,胆固醇概况和保险类型.

结论:

  • 与传统分数相比,机器学习模型在DOAC上的AF患者的出血风险提供了增强的预测能力.
  • ML模型可以识别新的风险因素,为更个性化的出血风险评估和管理策略铺平道路.
  • 这些发现支持将ML工具集成到临床实践中,以优化AF患者的抗凝治疗.