谷歌浏览器插件
订阅小程序
在清言上使用

Usefulness of P-wave peak time as an electrocardiographic parameter in predicting left ventricular diastolic dysfunction in patients with mitral regurgitation

Annals of Noninvasive Electrocardiology(2022)

引用 0|浏览3
暂无评分
摘要
Introduction Conventional Doppler measurements have limitations in predicting left ventricular diastolic dysfunction (LVDD) in patients with mitral regurgitation (MR). Recently, electrocardiographic P-wave peak time (PWPT) has been proposed as a parameter of detecting LVDD. This study aimed to evaluate the association between PWPT and left ventricular end-diastolic pressure (LVEDP) in patients with MR. Methods We performed echocardiography and cardiac catheterization in 82 patients with moderate or severe MR. We classified patients into two groups: low LVEDP group (L-LVEDP) (LVEDP <16 mmHg, n = 40) and high LVEDP group (H-LVEDP) (LVEDP >= 16 mmHg, n = 42). We evaluated LVDD and PWPT based on echocardiographic and electrocardiographic findings in both groups. Results The PWPT in lead II (PWPTII) was significantly longer in patients in the H-LVEDP group than in those in the L-LVEDP group (67 vs. 47 ms, p < .001). Using correlation analysis, LVEDP was positively correlated with PWPTII (r = .577, p < .001). Using multivariate analysis, PWPTII was found to be an independent predictor of increased LVEDP (95% CI: 0.1030-0.110; p < .001). Using receiver operating characteristic (ROC) curve analysis, the optimal cutoff value of PWPTII for predicting elevated LVEDP was 58.9 ms, with a sensitivity of 80.0% and a specificity of 73.8% (area under curve: 0.809, 95% CI: 0.713-0.905). Conclusion To the best of our knowledge, this is the first study to assess the effect of a significant valvular disease on PWPT in lead II. These findings suggest that prolonged PWPTII may be an independent predictor of increased LVEDP in patients with moderate or severe MR.
更多
查看译文
关键词
atrial pressure,echocardiography,electrocardiography,heart failure,left ventricular diastolic dysfunction,mitral valve regurgitation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要