Sarcopenia is a serious systemic disease that reduces overall survival. TAVI is selectively performed in patients with severe aortic stenosis who are not indicated for open cardiac surgery due to severe polymorbidity. Artificial intelligence-assisted body composition assessment from available CT scans appears to be a simple tool to stratify these patients into low and high risk based on future estimates of all-cause mortality. Within our study, the segmentation of preprocedural CT scans at the level of the lumbar third vertebra in patients undergoing TAVI was performed using a neural network (AutoMATiCA). The obtained parameters (area and density of skeletal muscles and intramuscular, visceral, and subcutaneous adipose tissue) were analyzed using Cox univariate and multivariable models for continuous and categorical variables to assess the relation of selected variables with all-cause mortality. 866 patients were included (median(interquartile range)): age 79.7 (74.9-83.3) years; BMI 28.9 (25.9-32.6) kg/m2. Survival analysis was performed on all automatically obtained parameters of muscle and fat density and area. Skeletal muscle index (SMI in cm2/m2), visceral (VAT in HU) and subcutaneous adipose tissue (SAT in HU) density predicted the all-cause mortality in patients after TAVI expressed as hazard ratio (HR) with 95% confidence interval (CI): SMI HR 0.986, 95% CI (0.975-0.996); VAT 1.015 (1.002-1.028) and SAT 1.014 (1.004-1.023), all p < 0.05. Automatic body composition assessment can estimate higher all-cause mortality risk in patients after TAVI, which may be useful in preoperative clinical reasoning and stratification of patients.

2nd Department of Internal Medicine St Anne's University Hospital in Brno Pekařská 53 656 91 Brno Czech Republic

Department of Physiology Faculty of Medicine Masaryk University Kamenice 5 625 00 Brno Czech Republic

Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava 17 Listopadu Street 2172 15 708 00 Ostrava Czech Republic

Faculty of Medicine Masaryk University Kamenice 5 625 00 Brno Czech Republic

Faculty of Medicine Palacky University Krizkovskeho 511 8 779 00 Olomouc Czech Republic

Hospital AGEL Třinec Podlesí Konská 453 739 61 Třinec Czech Republic

The Jackson Laboratory for Genomic Medicine 10 Discovery Drive Farmington CT 06032 USA

Sci Rep. 2024 May 10;14(1):10749. doi: 10.1038/s41598-024-61601-6. PubMed

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