Characteristics of a New Repolarization Descriptor Substituted for T-Wave Morphology Analysis in Patients With Cardiomyopathy and Myocardial Infarction

Abstract

ors et al assessed the orientation of the T-axis for cardiac risk stratification based on the theory of diagnostic vectorcardiography. 1 The T-axis was a strong and independent predictor of fatal and non-fatal cardiac events in the population of the Rotterdam study. Furthermore, the prognostic value of an abnormal T-axis has been proved to be greater than other electrocardiogram (ECG) risk factors of T-wave inversion and ST depression. However, the physiologic meaning of an abnormal T-axis remains unclear.Recently, T-wave morphology analysis (TMA) was hypothesized to quantify the irregularities of ventricular repolarization based on singular value decomposition of standard 12-lead ECGs. 2 TMA analysis assesses both ventricular depolarization and repolarization independent of the accuracy of measuring the repolarization process, such as QT interval and QT dispersion. Thus, TMA makes important the relationship between the QRS and T-wave vectors, and the morphological varieties of the T-wave.The total cosine R-to-T (TCRT) in the TMA descriptors is dependent on the spatial angle between depolarization and repolarization, in keeping with the concept of the ventricular gradient (VG). Zabel et al reported that the TCRT is the only descriptor of TMA that is a strong and independent predictor of adverse outcome in patients with myocardial infarction (MI). 3 Both the T-axis and TCRT are derived from the computed analysis of a vectrocardiographic ECG reconstructed from the standard 12-lead ECG. 1,2 A TCRT less than -0.888 is associated with increased 5-year cardiac mortality in a population with MI. 4 Furthermore, a TCRT less than -0.888 occurs in the case of a vectorcardiographic ECG with an angle >150 degrees between the QRS loop and the T-wave loop. Thus, we propose a new indicator relating the axes of QRS and T-waves on the surface ECG. In this study, we evaluated its characteristics in patients with cardiomyopathy (CM) or MI, which often present with T-wave inversions and ST depression. The aim of this study was to assess the correlation between TCRT and the axes of QRS and T-wave on the surface ECG in patients with these conditions. Methods SubjectsPatients with CM or MI were recruited from Nippon Medical School Hospital (Tokyo, Japan) and Yashio Heart Hospital (Saitama, Japan) from February 2002 to February Background Total cosine R-to-T (TCRT) is a descriptor of T-wave morphology analysis based on singular value decomposition of 12-lead electrocardiograms (ECGs), which is useful in risk stratification of patients with myocardial infarction (MI). A new marker of standard ECG substituted for TCRT is proposed and the aim of this study was to evaluate the correlation between the new index and TCRT in patients with cardiomyopathy and MI. Methods and ResultsPatients were divided into 2 groups: patients with cardiomyopathy (group CM, n=21, male =13), and patients with MI (group MI, n=36, male =28). TCRT was calculated using a custom software package. The ventricular gradient (VG)-index was defined as the total number of l...

Total cosine R‐to‐T for predicting ventricular arrhythmic and mortality outcomes: A systematic review and meta‐analysis

“…The total cosine R-to-T (TCRT) is a descriptor of T-wave morphology derived from computed analysis of vectorcardiograms that are reconstructed from the standard 12-lead electrocardiography (ECG) (Acar, Yi, Hnatkova, & Malik, 1999;Ono, Saitoh, & Itakura, 2006;Perkiomaki, Hyytinen-Oinas, & Karsikas, 2006). It is defined as the average of the cosines of the angles between the QRS and T vectors, reflecting the spatial difference between the depolarization and repolarization wavefronts (Belloch et al, 2007).…”

“…It is defined as the average of the cosines of the angles between the QRS and T vectors, reflecting the spatial difference between the depolarization and repolarization wavefronts (Belloch et al, 2007). TCRT can be determined with the eigenvalue method, principal component analysis (PCA) (Ono et al, 2006;Vicente, Johannesen, & Mason, 2015).…”