Identifying predictors of shear wave speed in cardiac shear wave elastography using multivariate analysis

Abstract Introduction Cardiac shear wave elastography (SWE) utilizes high-frame-rate ultrasound imaging to visualize the propagation of shear waves (SWs) following events such as mitral valve closure (MVC). The propagation velocities of these waves in the myocardium are directly related to myocardial stiffness, with higher speeds indicating stiffer tissue. SWE has shown potential for assessing myocardial stiffness. However, it remains unclear which factors other than myocardial stiffness influence SW speeds and the extent of their effects within a large and diverse patient cohort. Objective To identify independent echocardiography and demographic variables that influence SW speed after MVC, across a broad patient population. Methods Data from 473 subjects (age: 56 ± 22 years) were collected from our database, included healthy volunteers and patients with 10 conditions: cardiac amyloidosis, hypertrophic cardiomyopathy, arterial hypertension, heart transplantation, aortic stenosis, left bundle branch block, decompensated heart failure in the emergency department, childhood cancer survivors, patients scheduled for catheterization, and multiple sclerosis. All subjects underwent a standard echocardiography and SWE measurements. In the parasternal long axis view, SW speeds after MVC were assessed by placing anatomical M-modes along the interventricular septum. SWs appeared as tilted green bands using tissue acceleration maps, and their velocities were calculated from the slope in the spatiotemporal display (Figure 1). To identify the clinical and echocardiographic variables affecting SW speed, univariate and multivariable linear regression analysis were performed. Results Univariate analysis showed associations between SW speed with: age, systolic blood pressure (SBP), BMI, septal thickness (IVSd) , LV mass, end-diastolic volume (EDV), left atrial volume index (LAVI), global longitudinal strain (GLS), average e’, E/e’ and the presence of underlying cardiology pathology. Multivariate predictors included: LAVI, average e’, condition, LV mass, and BMI, mentioned in the order of importance (R=0.65; Figure 2). Conclusion These results indicate that SW velocity is influenced by both myocardial structure (e.g. LV mass and presence of pathology) and LV filling pressures (e.g. Average e’ and LAVI). Although some factors were consistent with previous studies, the current analysis of the expanded SWE database has identified new factors that need further investigation.