累积心电图异常预测左心室喷射率的减少:EchoNext数据库分析分析
Solomon Kim1, Beshoy Saad1, Gary Chu2
1California Northstate University College of Medicine, Elk Grove, CA, USA.
结合连续心电图 (ECG) 参数,如QRS持续时间和QT间隔 (QTc),可以比单个测量更好地预测左心室喷射率 (LVEF) 的降低. 然而,缺少的数据可能低估了这些心电图标记的真正预测能力.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 个体心电图 (ECG) 异常与左心室喷射率 (LVEF) 的减少有关.
- 对LVEF的多个连续ECG参数与单个参数或二进制评分的协同预测值尚未完全理解.
研究的目的:
- 评估结合连续心电图参数 (QRS持续时间,PR间隔,心率校正的QT间隔-QTc) 的预测值,以减少LVEF.
- 将多参数连续心电图模型的预测性能与单个心电图参数和二进制评分方法进行比较.
主要方法:
- 分析了来自EchoNext数据库的89,630个ECG-心电图配对研究.
- 开发和评估七个逻辑回归模型,使用训练 (70%) 和验证 (30%) 集,调整年龄和性别.
- 模型的评估使用了引导式置信区间和5倍分层交叉验证,并对缺失的数据模式进行了系统评估.
主要成果:
- 全连续心电图模型显示公平歧视 (AUC 0.710) 与单独的QTc (AUC 0.695) 相比.
- QTc具有最强的个人关联 (AUC 0.695),其次是QRS持续时间 (AUC 0.673) 和PR间隔 (AUC 0.603).
- 随着心电图异常的数量 (p < 0.001) 的增加,LVEF ≤45%的患病率显著增加 (p < 0.001).
结论:
- 与单个参数或二进制分类相比,组合的连续心电图参数为减少LVEF提供了增强的预测价值.
- 由于非随机缺失数据导致的显著选择偏差表明,观察到的关联可能低估了真正的预测能力.
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