Abstract

Abstract Background Right ventricular-pulmonary circulation coupling (RVPAC), which can be measured by the relation between tricuspid annular plane systolic excursion (TAPSE) and systolic pulmonary artery pressure (SPAP) by echocardiography, has been postulated as an independent prognostic factor of hospitalizations and mortality in heart failure (HF) patients. Purpose Our aim was to know the predictors of RVPAC improvement in a chronic HF cohort. Methods Retrospective analysis of a prospectively studied cohort of HF outpatients of different aetiologies attended in a multidisciplinary HF Unit. Prospectively scheduled echo-Doppler studies were performed at first visit and 1 year. A TAPSE/SPAP ratio <0.36 mm/mmHg was identified as the most deleterious. Significant RVPAC improvement at 1 year was defined as TAPSE/SPAP ratio ≥0.36 mm/mmHg together with a ≥10% improvement from baseline RVPAC. Multivariable logistic regression analysis (conditional backward stepwise) was performed to select variables independently associated with significant RVPAC improvement. A predictive model including age and the previously selected variables was created. Results From August 2001 to July 2017, 554 patients with TAPSE and SPAP data in the initial visit were included. Mean follow-up time was 4.6±3.7 years. At first visit 252 (45.5%) patients had RVPAC <0.36 mm/mmHg. Out of them, RVPAC at 1 year improved in 55 (21.8%). In multivariable analysis, the presence of baseline atrial fibrillation/flutter (OR 0.12 [95% CI 0.05–0.28], p<0.001), SPAP (OR 0.96 [95% CI 0.92–0.99], p=0.014) and female gender (OR 0.34 [95% CI 0.12–0.91], p=0.03) were related to lesser probability of RVPAC improvement at 1 year. A model with such variables, together with age, showed an AUC of 0.824 to predict significant RVPAC improvement. Conclusions Atrial fibrillation/flutter, increasing SPAP and female gender hamper RVPAC improvement at 1 year in HF patients with baseline TAPSE/SPAP ratio <0.36. Funding Acknowledgement Type of funding sources: None. Multivariate regression analysis

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