Physiologic Markers of Mortality in Acute Valve Syndrome: An Ischemic Physiology Score Stratifies Patient Risk

Background Acute valve syndrome (AVS) represents a high-risk phenotype of advanced valvular disease with largely uncharacterized risk heterogeneity. We aimed to validate an expanded AVS definition and develop a physiology-dependent risk stratification tool using available clinical markers. Methods This retrospective study analyzed 2380 patients undergoing aortic valve replacement for severe aortic stenosis and classified them as AVS (n = 1556) or progressive valvular disease (n = 824). The primary outcome was 1-year all-cause mortality. An L2-regularized logistic regression model was developed to predict 1-year mortality using admission laboratory markers and comorbidity burden. Model performance was assessed using nested cross-validation with calibration metrics including the Brier score. The constituent risk factors were aggregated into an Ischemic Physiology Score. Results AVS patients (65.4%) showed higher comorbidity burden and higher 1-year mortality (11.6 vs. 4.7%; p < 0.001) than progressive valvular disease. The prediction model performed well (area under the receiver operating characteristic curve [AUC]: 0.765 ± 0.043, Brier Score: 0.094) during five-fold cross-validation. The Ischemic Physiology Score, incorporating renal dysfunction, hyperlactatemia, liver injury, elevated natriuretic peptides, high comorbidity burden, and preoperative vasoactive support, stratified patients into 3 risk groups with 1-year mortality of 1.9% (0 factors), 9.7% (1 factor), and 25.5% (≥2 factors) ( p < 0.001 for trend). Multivariable analysis identified the Charlson Comorbidity Index, aspartate aminotransferase/alanine aminotransferase, lactate, N-Terminal pro-B type natriuretic peptide, and creatinine as independent predictors of 1-year mortality in our AVS cohort. Conclusions AVS is the predominant clinical phenotype within our cohort, appearing in over half of our patients undergoing aortic valve replacement. Complementing existing surgical risk scores to guide intervention optimization, a physiologic score based on admission data effectively stratifies patient risk.