Perez Marco V, Dewey Frederick E, Marcus Rachel, Ashley Euan A, Al-Ahmad Amin A, Wang Paul J, Froelicher Victor F
Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Am Heart J. 2009 Oct;158(4):622-8. doi: 10.1016/j.ahj.2009.08.002.
Atrial fibrillation (AF) is the most prevalent arrhythmia in the United States and accounts for more than 750,000 strokes per year. Noninvasive predictors of AF may help identify patients at risk of developing AF. Our objective was to identify the electrocardiographic characteristics associated with onset of AF.
This was a retrospective cohort analysis of 42,751 patients with electrocardiograms (ECGs) ordered by physician's discretion and analyzed using a computerized system. The population was followed for detection of AF on subsequent ECGs. Cox proportional hazard regression analysis was performed to test the association between these ECG characteristics and development of AF.
For a mean follow-up of 5.3 years, 1,050 (2.4%) patients were found to have AF on subsequent ECG recordings. Several ECG characteristics, such as P-wave dispersion (the difference between the widest and narrowest P waves), premature atrial contractions, and an abnormal P axis, were predictive of AF with hazard ratio of approximately 2 after correcting for age and sex. P-wave index, the SD of P-wave duration across all leads, was one of the strongest predictors of AF with a concordance index of 0.62 and a hazard ratio of 2.7 (95% CI 2.1-3.3) for a P-wave index >35. These were among the several independently predictive markers identified on multivariate analysis.
Several ECG markers are independently predictive of future onset of AF. The P index, a measurement of disorganized atrial depolarization, is one of the strongest predictors of AF. The ECG contains valuable prognostic information that can identify patients at risk of AF.
心房颤动(AF)是美国最常见的心律失常,每年导致超过75万例中风。房颤的非侵入性预测指标可能有助于识别有发生房颤风险的患者。我们的目的是确定与房颤发作相关的心电图特征。
这是一项对42751例患者进行的回顾性队列分析,这些患者的心电图(ECG)由医生酌情开具,并使用计算机系统进行分析。对该人群进行随访,以检测后续心电图上的房颤情况。进行Cox比例风险回归分析,以检验这些心电图特征与房颤发生之间的关联。
平均随访5.3年,发现1050例(2.4%)患者在后续心电图记录中出现房颤。一些心电图特征,如P波离散度(最宽和最窄P波之间的差异)、房性早搏和异常P轴,在校正年龄和性别后,对房颤具有预测性,风险比约为2。P波指数,即所有导联P波持续时间的标准差,是房颤最强的预测指标之一,一致性指数为0.62,P波指数>35时风险比为2.7(95%CI 2.1-3.3)。这些是多变量分析中确定的几个独立预测标志物。
几种心电图标志物可独立预测未来房颤的发作。P指数,一种衡量心房去极化紊乱的指标,是房颤最强的预测指标之一。心电图包含有价值的预后信息,可识别有房颤风险的患者。