Longitudinal strain by speckle tracking and echocardiographic parameters as predictors of adverse cardiovascular outcomes in chronic Chagas cardiomyopathy

Data de publicació electrònica: 13-01-2022To analyze the prognostic value of left ventricular global longitudinal strain (LV-GLS) and other echocardiographic parameters to predict adverse outcomes in chronic Chagas cardiomyopathy (CCM). Prospective cohort study conducted in 177 consecutive patients with different CCM stages. Transthoracic echocardiography measurements were obtained following the American Society of Echocardiography recommendations. By speckle-tracking echocardiography, LV-GLS was obtained from the apical three-chamber, apical two-chamber, and apical four-chamber views. The primary composite outcome (CO) was all-cause mortality, cardiac transplantation, and a left ventricular assist device implantation. After a median follow-up of 42.3 months (Q1 = 38.6; Q3 = 52.1), the CO incidence was 22.6% (95% CI 16.7-29.5%, n = 40). The median LV-GLS value was - 13.6% (Q1 = - 18.6%; Q3 = - 8.5%). LVEF, LV-GLS, and E/e' ratio with cut-off points of 40%, - 9, and 8.1, respectively, were the best independent CO predictors. We combined these three echocardiographic markers and evaluated the risk of CO according to the number of altered parameters, finding a significant increase in the risk across the groups. While in the group of patients in which all these three parameters were normal, only 3.2% had the CO; those with all three abnormal parameters had an incidence of 60%. We observed a potential incremental prognostic value of LV-GLS in the multivariate model of LVEF and E/e' ratio, as the AUC increased slightly from 0.76 to 0.79, nevertheless, this difference was not statistically significant (p = 0.066). LV-GLS is an important predictor of adverse cardiovascular events in CCM, providing a potential incremental prognostic value to LVEF and E/e' ratio when analyzed using optimal cut-off points, highlighting the potential utility of multimodal echocardiographic tools for predicting adverse outcomes in CCM