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使用电子健康记录数据预测中风后的认知障碍.

Jeffrey M Ashburner1,2, Yuchiao Chang1,2, Bianca Porneala1

  • 1Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.

medRxiv : the preprint server for health sciences
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概括

早期识别患中风后认知障碍 (PSCI) 风险较高的个体对于二次预防至关重要. 使用电子健康记录数据的预测模型准确地识别了需要修改风险因素以减少PSCI的患者.

关键词:
在中风后的认知障碍.风险预测风险预测风险分层的分层化

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科学领域:

  • 神经学 神经学
  • 公共卫生 公共卫生
  • 数据科学数据科学数据科学

背景情况:

  • 脑卒中后认知障碍 (PSCI) 的二次预防需要早期识别有风险的个体.
  • 电子健康记录 (EHR) 为开发预测模型提供了丰富的数据来源.

研究的目的:

  • 开发和验证一种预测模型,用于在中风后5年内识别患有PSCI风险增加的患者.
  • 使用易于获取的电子健康记录 (EHR) 数据进行风险预测.

主要方法:

  • 采用了一个队列研究设计,招募了45岁以上的患者,他们在2003-2016年期间发生了缺血性中风.
  • 从EHR中提取了PSCI的预测因素,并使用LASSO惩罚的Cox比例危险模型进行变量选择.
  • 该模型经过内部和外部验证,使用与学术医疗中心关联的初级保健实践的数据.

主要成果:

  • 预测模型表现良好,c统计值为0.731 (内部验证) 和0.724 (外部验证).
  • 风险评分将患者分为低风险,中等风险和高风险组.
  • 高风险人群中,发生PSCI事件的危险比率显著增加 (HR:6.2内部,6.1外部).

结论:

  • 常规收集的EHR数据可以准确预测5年的PSCI风险.
  • 开发的模型允许针对性预防干预的风险分层.
  • 这种方法有助于早期识别那些从风险因素修改中受益的人.