Sex-Specific Left Atrial Strain Thresholds Predict Outcomes in Aortic Stenosis.
BACKGROUND Left atrial (LA) remodeling represents an integral marker of cardiac damage in aortic stenosis (AS). While the LA volume index (LAVi) reflects chronic pressure overload, left atrial reservoir strain (LARS) by speckle-tracking echocardiography detects early LA dysfunction. OBJECTIVES To evaluate the prognostic value of LAVi and LARS in AS, define optimal cut-offs associated with adverse outcomes, and assess whether sex-specific thresholds enhance risk stratification. METHODS Retrospective analysis of 552 outpatients (50.4% men, mean age 81±8 years) with at least moderate AS and in sinus rhythm. The composite endpoint was all-cause mortality or hospitalization for heart failure (HHF). Optimal cut-offs for LAVi and LARS were derived using 2-year time-dependent ROC analysis and validated in an independent external cohort of 192 patients (46% men). Associations with outcomes were examined using multivariable Cox regression adjusted for age, NYHA class, and LVEF. RESULTS Over a median follow-up of 17.4 months, 118 patients (21.1%) experienced the composite endpoint. In univariable analyses, both larger LAVi and lower LARS were associated with adverse outcomes (both p<0.001). However, only LARS retained prognostic significance after multivariable adjustment. Sex-specific analyses identified LARS<13% in women and <19% in men as the optimal thresholds discriminating higher risk. In multivariable Cox models, reduced sex-specific LARS remained independently associated with adverse outcomes in both sexes (women: HR 1.82 [95% CI 1.04-3.18], p=0.03; men: HR 1.90 [95% CI 1.01-3.60], p=0.045), whereas LAVi lost significance (p≥0.19). These results were consistently reproduced in the external validation cohort (p < 0.05). CONCLUSIONS Sex-specific LARS thresholds independently predict mortality and HHF in AS, outperforming LAVi and universal cut-offs. Incorporating LA strain assessment into AS risk stratification may enhance prognostic precision and support personalized management across sexes.