Patient-specific computational models constructed from non-invasive measurements can estimate LV dyssynchrony and predict long-term reverse remodelling after CRT (P<0.001).
Observational
Do patient-specific computational models constructed from non-invasive measurements predict long-term reverse ventricular remodelling after CRT in patients with dyssynchronous heart failure?
Patient-specific computational models using non-invasive measurements can estimate LV dyssynchrony and predict long-term reverse remodeling after CRT, potentially performing as well as electroanatomic mapping.
p-value: p=<0.001
AIMS: Left ventricular activation delay due to left bundle branch block (LBBB) is an important determinant of the severity of dyssynchronous heart failure (DHF). We investigated whether patient-specific computational models constructed from non-invasive measurements can provide measures of baseline dyssynchrony and its reduction after CRT that may explain the degree of long-term reverse ventricular remodelling. METHODS AND RESULTS: 0.92, P < 0.001). Models also suggested that optimizing VV delays may improve resynchronization by this measure of activation delay. CONCLUSIONS: Patient-specific computational models constructed from non-invasive measurements can compute estimates of LV dyssynchrony and their changes after CRT that may be as good as or better than electroanatomic mapping for predicting long-term reverse remodelling.
Villongco et al. (Mon,) conducted a observational in Dyssynchronous heart failure (DHF) with LBBB. Patient-specific computational models from non-invasive measurements vs. Electroanatomic mapping was evaluated on Long-term reverse ventricular remodelling (p=<0.001). Patient-specific computational models constructed from non-invasive measurements can estimate LV dyssynchrony and predict long-term reverse remodelling after CRT (P<0.001).