Aortic-valve calcium score for the diagnosis of severe aortic stenosis: A systematic review and meta-analysis

Background Aortic stenosis (AS) is the most common valvular disease. Nowadays, the degenerative process including valve calcification is the main etiology. Severe AS is associated with poor prognosis and is an indication for aortic-valve intervention. While echocardiography is the main imaging tool to diagnose and quantify AS severity, computed tomography can also be used in difficult cases, and the aortic-valve calcification score is proposed as a proxy to quantify the disease severity. Nevertheless, different thresholds are proposed to grade AS. We performed systematic review and meta-analysis of studies using computed tomography to diagnose severe AS. Method Up to 30/11/2018, the key terms “aortic-valve”, “stenosis”, “computed tomography”, “calcification” or “calcium” were searched in PubMed. Our predefined inclusion criteria were clinical studies to diagnose severe AS by aortic-valve calcification derived from computed tomography. Studies including only native valves and providing cut-off levels for stenosis severity were included. The Agatston method were used and expressed in Agatston unit. Results Eleven from 534 studies, including 4,529 patients, were eligible. Population, AS severity, acquisition modalities and results were heterogenous. Aortic-valve calcification score was accurate to diagnose severe AS (pooled area under the curve = 0.89 ± 0.02). The cut-off levels differed between studies. With meta-analysis, the optimal cut-off was 1,648 AU for general population. Only three studies provided a cut-off per gender, with optimal value derived from meta-analysis of 1,354 AU for women and 2,048 AU for men. Conclusion Aortic-valve calcification score derived from computed tomography is useful to diagnose severe AS. It can provide supplementary data for evaluation of AS in patients with discordant echocardiographic findings. However, individual participant data meta-analysis is required to identify the most appropriate cut-off values with better accuracy.