Association Between Aortic Valve Mean Gradient and TAVR Outcomes: A Perspective From a Nationwide Registry.
Abstract
Background
Patients with severe aortic stenosis with very low resting aortic valve mean gradient (MG) are underrepresented in transcatheter aortic valve replacement (TAVR) studies. We investigated the association of resting aortic valve MG with post-TAVR outcomes in a large nationwide cohort.
Methods
We included patients from the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry with a self-expanding Evolut TAVR (until December 2022) and eligible for 1-year follow-up without prior balloon aortic valvuloplasty, TAVR, or surgical aortic valve replacement.
Results
Among 80 429 patients (mean age, 80.7±7.9; women, 53.9%; high/extreme surgery risk, 51.2%) stratified into 6 echocardiographic resting aortic valve MG groups (10-<20, 20-<25, 25-<30, 30-<35, 35-<40, ≥40 mmHg), 1 year, all-cause mortality increased progressively across decreasing MG subgroups (from 9.3%, ≥40 mmHg to 20.9%, 10-<20 mmHg; P<0.001; 10.8% entire cohort), as did hospital readmission. Kansas City Cardiomyopathy Questionnaire scores increased progressively across increasing MG subgroups (P<0.001 at all time points). The Valve Academic Research Consortium 3 Kansas City Cardiomyopathy Questionnaire ordinal outcome improved substantially/moderately for all subgroups (1 year ≥52% patients). After multiple imputation for missing covariate data, Cox regression adjusting for clinically relevant baseline factors demonstrated a significant association between baseline MG and 1-year mortality (hazard ratio, 0.93 [95% CI, 0.91-0.94]; P<0.001).
Conclusions
Baseline aortic valve MG is an important predictor of 1-year all-cause mortality in patients with severe aortic stenosis undergoing TAVR. Most patients undergoing TAVR had considerably improved quality-of-life assessments regardless of resting MG. Additional studies should elucidate mechanisms associated with MG-mortality risk and whether improved patient selection can further improve outcomes.