The Prognostic Value of Right Ventricle–Pulmonary Artery Coupling in Valve Interventions
Vitaliy Androshchuk, Edouard Long, Omar Chehab, Natalie Montarello, Joshua Wilcox, Benedict McDonaugh, Ronak Rajani, Philippe Pîbarot, Bernard Prendergast, Tiffany Patterson, Simon Redwood
Abstract
BACKGROUND: Right ventricle-pulmonary artery (RV-PA) coupling is prognostically important in valvular heart disease. OBJECTIVES: The authors performed a systematic review and meta-analysis to quantify the association of RV-PA coupling with clinical endpoints after intervention for aortic stenosis (AS), mitral regurgitation (MR), and tricuspid regurgitation (TR). METHODS: The primary outcome was all-cause mortality, and the secondary outcome was a composite of major adverse cardiovascular events (MACE). A random-effects model was used to compute pooled effect estimates, and summary receiver-operating characteristic curves identified optimal RV-PA thresholds. RESULTS: In total, 30 interventional studies (N = 12,992) met eligibility criteria, including 14 AS (n = 6,100), 12 MR (n = 5,032), and 4 TR (n = 1,860) studies. Tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP) was the most studied RV-PA coupling index. Reduced TAPSE/PASP was independently associated with all-cause mortality (AS adjusted HR: 1.69 [95% CI: 1.30-2.20]; MR adjusted HR: 1.94 [95% CI: 1.40-2.69]; P < 0.001) and the composite MACE (AS adjusted HR: 1.60 [95% CI: 1.29-2.00]; MR adjusted HR: 2.01 [95% CI: 1.54-2.62]; P < 0.001). There were significant nonlinear associations between TAPSE/PASP and adverse outcomes in AS and MR (P < 0.001). There were insufficient data to estimate a pooled effect-size in TR. Optimal TAPSE/PASP thresholds to predict all-cause mortality were ≤0.51 mm/mm Hg for AS interventions, ≤0.33 mm/mm Hg for MR interventions and ≤0.44 mm/mm Hg for TR interventions. CONCLUSIONS: TAPSE/PASP is an independent predictor of outcomes after interventions for AS and MR. The disease-specific TAPSE/PASP cutoffs could be integrated into risk-stratification models to better predict mortality before valve interventions and improve patient selection.