Structural heart : the journal of the Heart Team · 2025
Physiologic Markers of Mortality in Acute Valve Syndrome: An Ischemic Physiology Score Stratifies Patient Risk.
СтатьяНазвание и аннотация: Перевод в очереди
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Saleh Omar, Valle Nicholas J, Saleh Israa, Benza Raymond, Talreja Deepak R, Summers Matthew R
Аннотация
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.
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.