Abstract 12411: Effect of Adding E/e′ Ratio to Risk Prediction in Chagas Cardiomyopathy
Abstract: The value of the E/e′ ratio as a reliable estimate of LA pressure has recently been questioned. This study sought to investigate the improvement in risk prediction by adding E/e′ ratio to the model with established parameters predictors of outcome in Chagas cardiomyopathy.
Methods: A total of 232 patients with Chagas cardiomyopathy (mean age 48 ± 12 years; 143 male) were prospectively enrolled. End points were death or cardiac transplantation.
Results: During a mean follow-up of 3.4 years, 102 patients died and 11 patients underwent cardiac transplantation. The overall cardiac events rate was 13.2 per year. Risk factors for short-term mortality included NYHA functional class (hazard ratio [HR]: 2.16, 95% confidence interval [CI]: 1.15 to 4.06), LV ejection fraction (HR: 0.89, 95% CI: 0.84 to 0.96), RV Tei index, per 0.1 unit increase (HR: 1.14, 95% CI: 1.07 to 1.22), and LA volume index, per 1-mL/m2 increase (HR: 1.03, 95% CI: 1.01 to 1.05). The inclusion of E/e′ ratio (HR: 0.84, 95% CI: 0.72 to 0.98) and the statistical interaction term (E/e′ ratio and LV ejection fraction) (HR: 1.01, 95% CI: 1.01 to 1.01) have improved a prediction of the model. The net reclassification improvement (NRI) of adding E/e′ ratio resulted in the improvement for predicting 1-year mortality, with the net gain in reclassification proportion of 0.122 (p=0.0002). Integrated discrimination improvement (IDI) estimated at 0.0387 was also statistically significant (p=0.0019). Bootstrap validation demonstrated good internal validation of the model without E/e′ (c-index 0.834, 95% CI: 0.788 to 0.888) and with E/e′ (c-index 0.838, 95% CI: 0.768 to 0.877).
Conclusions: Inclusion of E/e′ ratio in the prediction model resulted in more accurate risk classification. Based on the NRI and its components, the addition of E/e′ lead to a substantial improvement in reclassification, especially for individuals with non-events. After calibration and internal validation, our final model showed excellent performance to accurately stratify patients into clinically relevant risk categories.
- © 2010 by American Heart Association, Inc.