Background: Studies of echocardiographic predictors of response after cardiac resynchronization therapy (CRT) have largely involved single parameters. We hypothesized that combining parameters would be more robust and sought to develop a multiparametric echocardiographic score for predicting CRT response.

Methods and Results: Global longitudinal strain of left ventricle was added to standard echocardiographic measurements in 334 consecutive patients (224 men; mean, 65 ± 12 years) who underwent baseline echocardiography before CRT and underwent follow-up echocardiograms at 1 year. Regression analysis was performed to create an echocardiographic score for prediction of LV reverse remodeling (defined as ≥ 15% reduction in the LV end-systolic volume). Cox proportional hazards models were used to identify the association of the score with death, transplantation or LV assist device implantation, and heart failure hospitalization during 57 ± 22 months of follow-up. LV reverse remodeling (n = 161; 48%) was associated with pre-CRT LV end-diastolic dimension index < 3.1 cm/m², global longitudinal strain of left ventricle < –7%, left atrial area < 26 cm², right ventricular end-diastolic area index < 10.0 cm²/m², right atrial area < 20 cm², and right ventricular fractional area change ≥ 35%. Combination of these into an echocardiographic score allowed prediction of LV reverse remodeling with a sensitivity of 84% and a specificity of 79%. During follow-up, there were 134 deaths, 18 heart transplantations/LV assist device implantations, and 93 heart failure admissions. The score was associated with heart failure admission, heart transplantation/LV assist device, or death (hazard ratio, 0.97; 95% confidence interval, 0.95–0.98; P < 0.001) and all-cause death (hazard ratio, 0.97; 95% confidence interval, 0.96–0.98; P < 0.001), independent of age, sex, ischemic cause, and initial functional class.

Conclusions: A multiparametric echocardiographic score is helpful in selecting patients likely to undergo reverse remodeling after CRT and predicts clinical outcomes.