Prediction models incorporating echocardiographic, ECG, and biomarker data can help identify asymptomatic patients with moderate to severe aortic stenosis who may benefit from early aortic valve replacement.
Outcomes in patients with moderate to severe aortic stenosis (AS) from a community hospital cohort are similar those reported from tertiary institutions. Echocardiographic measures of the severity of obstruction are key variables in predicting outcomes. Additional variables that are not direct measures of AS severity are associated with adverse events. A reduced left ventricular (LV) ejection fraction forecasts a less favorable outcome yet most asymptomatic patients have preserved LV ejection fractions. Consequently, other measures of LV systolic function associated with adverse outcomes are important; namely, reduced LV longitudinal shortening and increased LV mass. Diastolic function can be compromised in AS indicated by either elevated E/ e or left atrial enlargement; both are associated with unfavorable outcomes. Electrocardiographic evidence of LV strain reflects LV mid-wall fibrosis and identifies patients with adverse outcomes. Biomarkers including elevated values of high sensitivity troponin I and BNP contribute to models predicting outcomes. Prediction models help identify asymptomatic patients with quite unfavorable prognoses who may benefit from early aortic valve replacement.
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Robert C. Bahler (2018) studied this question.
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