Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00080716" target="_blank" >RIV/00159816:_____/24:00080716 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/24:00135825 RIV/61989100:27240/24:10257244

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-024-59134-z" target="_blank" >https://www.nature.com/articles/s41598-024-59134-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-59134-z" target="_blank" >10.1038/s41598-024-59134-z</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI

Popis výsledku v původním jazyce

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/m(2). Survival analysis was performed on all automatically obtained parameters of muscle and fat density and area. Skeletal muscle index (SMI in cm(2)/m(2)), 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 &lt; 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.

Název v anglickém jazyce

Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI

Popis výsledku anglicky

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/m(2). Survival analysis was performed on all automatically obtained parameters of muscle and fat density and area. Skeletal muscle index (SMI in cm(2)/m(2)), 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 &lt; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30200 - Clinical medicine

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

8842

Kód UT WoS článku

001205348700026

EID výsledku v databázi Scopus

—