基于明尼苏达州代码分类系统的心电扫描得分预测了无症状低风险人群中心血管死亡率
Wook-Dong Kim1, Yonggu Lee1, Byung Sik Kim1
1Department of Internal Medicine, Division of Cardiology, Hanyang University Guri Hospital, Guri City, Republic of Korea.
Annals of medicine
|December 5, 2023
概括
使用多个异常发现的新型心电图 (ECG) 评分系统可以预测一般人群中的心血管死亡风险. 这种心电图评分独立预测风险,并改进了现有的风险估计器,以更好地预测心血管事件.
科学领域:
- 心脏病学 心脏病学
- 预防医学 预防医学
- 生物统计学 生物统计学
背景情况:
- 单个异常心电图 (ECG) 发现在低风险人群中对心血管 (CV) 事件的预测能力较低.
- 结合多个心电图异常来预测心血管死亡的评分系统的实用性需要进一步评估.
研究的目的:
- 评估一种新的心电图评分系统在一般人群中对心血管疾病死亡的预测价值.
- 为了确定ECG评分是否能改善风险分层,超越传统的风险因素.
主要方法:
- 一项基于社区的前性队列研究跟踪了8417名没有先前存在动脉样性心血管疾病 (ASCVD) 的参与者,持续了18年.
- 使用明尼苏达州代码分类分析了标准的12导电心电图;通过国家统计数据追踪了心血管死亡.
- 使用多变量Cox比例危险模型来识别显著的ECG发现并制定预测得分.
主要成果:
- 多个心电图异常 (例如,ST-T波变化,心房动,Q波,轴偏差) 与心血管死亡风险增加有关.
- 开发的ECG评分显示了适度的预测值 (C统计0.6320.760) 和独立预测心血管死亡.
- 结合传统风险因素 (哈雷尔C指数:0.852) 的ECG得分,超过了现有的风险估计.
结论:
- 结合多个异常发现的心电图评分系统可以独立预测无症状,低风险个体的心血管死亡.
- 这种心电图分数为传统风险估计器提供了附加值,增强了心血管风险分层.
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