PEKAŘ, Matej, Otakar JIRAVSKÝ, Jan NOVÁK, Piotr BRANNY, Jakub BALUŠÍK, Daniel DANIŠ, Jan HEČKO, Marek KANTOR, Robert PROSECKÝ, Lubomír BLAHA and Radek NEUWIRTH. Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI. SCIENTIFIC REPORTS. BERLIN: NATURE PORTFOLIO, 2024, vol. 14, No 1, p. 1-9. ISSN 2045-2322. Available from: https://dx.doi.org/10.1038/s41598-024-59134-z.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI
Authors PEKAŘ, Matej (703 Slovakia, guarantor, belonging to the institution), Otakar JIRAVSKÝ (203 Czech Republic), Jan NOVÁK (203 Czech Republic, belonging to the institution), Piotr BRANNY (203 Czech Republic), Jakub BALUŠÍK (203 Czech Republic), Daniel DANIŠ (203 Czech Republic), Jan HEČKO (203 Czech Republic), Marek KANTOR (203 Czech Republic), Robert PROSECKÝ (203 Czech Republic, belonging to the institution), Lubomír BLAHA (203 Czech Republic) and Radek NEUWIRTH (203 Czech Republic).
Edition SCIENTIFIC REPORTS, BERLIN, NATURE PORTFOLIO, 2024, 2045-2322.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 30201 Cardiac and Cardiovascular systems
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 4.600 in 2022
Organization unit Faculty of Medicine
Doi http://dx.doi.org/10.1038/s41598-024-59134-z
UT WoS 001205348700026
Keywords in English Sarcopenia; Artifcial intelligence; Visceral adipose tissue; Subcutaneous adipose tissue; Survival; TAVI
Tags 14110116, 14110515, rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Tereza Miškechová, učo 341652. Changed: 21/5/2024 08:54.
Abstract
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.
Links
MUNI/A/1547/2023, interní kód MUName: Analýza (dys)funkce: od molekul k živému organismu
Investor: Masaryk University, Analysis of (dys)function: from molecules to the living organism
MUNI/A/1555/2023, interní kód MUName: Patofyziologie, epidemiologie, diferenciální diagnostika a odhad prognózy vybraných vnitřních nemocí
Investor: Masaryk University, Pathophysiology, epidemiology, differential diagnostics and prognostic stratification in selected internal diseases
PrintDisplayed: 10/7/2024 21:08