Myocardial Performance Index By Tissue Doppler Echocardiography Predicts Adverse Events In Patients With Atrial Fibrillation

EUROPEAN HEART JOURNAL(2020)

引用 5|浏览8
暂无评分
摘要
Aims The prognostic value of myocardial performance index (MPI) has not yet been assessed in patients with atrial fibrillation (AF). The aim of this study was to evaluate the prognostic value of MPI by tissue Doppler imaging (TDI) M-mode in AF patients.Methods and results Echocardiograms from 210 patients with AF during examination were analysed offline. Patients with known heart failure (HF) were excluded. Time intervals were measured using an M-mode line through the mitral valve leaflets to provide a colour diagram of the mitral leaflet movement so all time intervals could be measured from one cardiac cycle. MPI was calculated as the sum of isovolumic relaxation time and isovolumic contraction time divided by the ejection time [(IVRT+IVCT)/ET]. During a median follow-up of 2.4 years, 84 patients (40%) reached the combined endpoint of major adverse cardiovascular events (MACE), being all-cause mortality, HF, myocardial infarction, or stroke. Increasing MPI was significantly associated with an increased risk of MACE, and the risk increased with 20% per 0.1 increase in MPI [hazard ratio (HR) 1.20, 95% confidence interval (CI) 1.10-1.32; P < 0.001]. Increasing MPI was also significantly associated with a lower left ventricular ejection fraction (LVEF) (P < 0.001). Nevertheless, MPI remained an independent predictor even after adjustment for age, sex, diabetes mellitus, left atrial volume, and LVEF (HR 1.12, 95% CI 1.01-1.25; P = 0.038).Conclusion Increasing MPI was significantly associated with increased risk of MACE and remained an independent predictor after multivariabte adjustment. This demonstrates that the MPI obtained by TDI M-mode might be useful in assessing cardiac function in AF patients with ongoing arrhythmia during examination.
更多
查看译文
关键词
echocardiography,tissue Doppler imaging,time intervals,atrial fibrillation,outcome,mortality
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