AUTHOR=Huang Yan , Wang Yiming , Guo Junfang , Fang Ping , Kong Biao , Wang Zilan , Zuo Junbo 

TITLE=Impact of chest computed tomography-determined low skeletal muscle mass on the survival of patients with acute heart failure

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2025.1569681

DOI=10.3389/fcvm.2025.1569681

ISSN=2297-055X

ABSTRACT=BackgroundComputed tomography (CT)-determined low skeletal muscle mass (SMM) has been reported to be associated with poor clinical outcomes in various diseases; however, limited information is available regarding patients with heart failure (HF). Our research aimed to investigate the prognostic significance of low SMM assessed by chest CT scans in hospitalized patients with acute heart failure (AHF).MethodsSkeletal muscle index (SMI) was assessed using chest CT scans at the twelfth thoracic vertebra (T12) level, and sex-specific optimal cutoff values for T12 SMI were determined using the X-tile program based on all-cause mortality. The outcomes of this study were all-cause death and cardiovascular death. Cox proportional hazards models were employed to identify the risk factors for mortality.ResultsThis study enrolled 305 inpatients with AHF (62.3% males). According to the optimum cutoff value (31.9 cm2/m2 for females and 40.6 cm2/m2 for males), a total of 154 patients (50.5%) had low SMM. Kaplan–Meier survival analysis showed that patients with low SMM had a higher likelihood of experiencing all-cause death (p < 0.001) and cardiovascular death (p < 0.001) compared to those with normal SMM. Furthermore, multivariable Cox regression analysis revealed that low SMM was independent risk factors associated with all-cause death [hazard ratio (HR) = 2.37, 95% CI: 1.51–3.73; p < 0.001] and cardiovascular death (HR = 3.06, 95% CI: 1.75–5.37; p < 0.001).ConclusionsChest CT-determined low SMM has the potential to serve as a valuable imaging prognostic indicator for predicting adverse outcomes in AHF patients.