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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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Published on: July 14, 2023

使用电子健康记录数据预测中风后的认知障碍.

Jeffrey M Ashburner^1,2, Yuchiao Chang^1,2, Bianca Porneala¹

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

International journal of stroke : official journal of the International Stroke Society

|March 28, 2024

概括

此摘要是机器生成的。

一个新的预测模型使用电子健康记录准确地识别出患中风后认知障碍 (PSCI) 高风险的个体. 该工具有助于早期干预和风险因素修改,以改善患者的治疗结果.

关键词:

缺血性中风是因为缺血性中风.在中风后的认知障碍.风险预测风险预测风险分层的分层是风险分层.

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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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科学领域:

神经学神经学
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

脑卒中后认知障碍 (PSCI) 构成了重大挑战,需要及早识别有风险的个体,以进行有针对性的二次预防.
有效的风险因素修改策略对于减少PSCI的发病率和严重程度至关重要.

研究的目的:

开发和验证一种预测模型,用于在中风后五年内识别患PSCI风险较高的患者.
利用电子健康记录 (EHR) 中随时可用的数据用于模型开发和应用.

主要方法:

一项队列研究,涉及来自两个学术医疗中心的45岁以上的初级保健患者,在2003-2016年 (发展/内部验证) 和2010-2022年 (外部验证) 之间发生了缺血性中风.
从EHR中提取了PSCI的预测因素;结果被定义为使用ICD-9/10代码在中风后五年内发生的痴呆症/认知障碍.
最少绝对收缩和选择运营商 (LASSO) 处罚了考克斯的比例危险模型,使用了10倍的交叉验证来进行变量选择.

主要成果:

预测模型表现良好,内部验证队列的C统计值为0.731,外部验证队列的C统计值为0.724.
风险评分将患者分为低风险,中等风险和高风险组.
与低风险人群相比,高风险人群对发生PSCI的危险比率 (HR:6.2内部,6.1外部) 显著增加 (HR:6.2内部,6.1外部).

结论:

常规收集的电子健康记录数据可以准确预测中风后认知障碍的五年风险.
开发的模型为风险分层提供了有价值的工具,使PSCI风险增加的个体能够进行有针对性的预防性干预.