Your CT scans are hiding crucial TAVI survival data: Are you looking?

Transcatheter Aortic Valve Implantation (TAVI) has revolutionized treatment for severe aortic stenosis, but optimal patient selection remains challenging. This commentary highlights findings from our recent systematic review of 14 studies comprising 9692 TAVI patients, which revealed that CT-derived adipose tissue parameters provide valuable prognostic information often overlooked during procedural planning. We found that higher subcutaneous adipose tissue consistently associated with better survival, while adipose tissue quality, measured by CT attenuation, proved equally important. The relationship between adiposity and outcomes appears U-shaped rather than linear, with both extremely low and high adiposity quartiles correlating with worse outcomes, while moderate subcutaneous adiposity provides optimal outcomes by offering metabolic reserves without pathological complications. Notably, fat distribution patterns (VAT:SAT ratio < 1) were associated with better cardiovascular outcomes, underscoring that where fat is stored matters more than total quantity. The obesity-dependent effects of visceral adipose tissue reflect fundamental differences in metabolic physiology: in non-obese patients, modest VAT represents protective energy reserves, while in obese patients, lower VAT indicates relatively better metabolic health within the context of existing obesity. These adipose tissue characteristics are readily available in pre-procedural CT scans already used for anatomical assessment, requiring minimal additional resources while potentially enhancing risk stratification. We present a novel decision algorithm with sex-specific thresholds that enables immediate clinical implementation of these measurements for patient risk stratification. As TAVI indications expand to include both increasingly frail elderly patients and those at intermediate surgical risk, integrating these overlooked adipose tissue parameters into clinical decision-making could improve patient selection and outcomes.