Pectoralis major muscle quantification by cardiac MRI is a strong predictor of major events in HF
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Updated: Oct 11, 2025

MRI and PET in Mouse Models of Myocardial Infarction
Published on: December 19, 2013
Gonçalo J L Cunha1, Bruno M L Rocha2, Pedro Freitas2
1Cardiology Department, Hospital de Santa Cruz, Centro Hospitalar Lisboa Ocidental, Av. Prof. Dr. Reinaldo dos Santos, Carnaxide, 2790-134, Lisbon, Portugal. g.cunha@campus.ul.pt.
Abstract:
Clinical overt cardiac cachexia is a late ominous sign in patients with heart failure (HF) and reduced left ventricular ejection fraction (LVEF). The main goal of this study was to assess the feasibility and prognostic significance of muscle mass quantification by cardiac magnetic resonance (CMR) in HF with reduced LVEF. HF patients with LVEF < 40% (HFrEF) referred for CMR were retrospectively identified in a single center. Key exclusion criteria were primary muscle disease, known infiltrative myocardial disease and intracardiac devices. Pectoralis major muscles were measured on standard axial images at the level of the 3rd rib anteriorly. Time to all-cause death or HF hospitalization was the primary endpoint. A total of 298 HF patients were included (mean age 64 ± 12 years; 76% male; mean LVEF 30 ± 8%). During a median follow-up of 22 months (IQR: 12-33), 67 (22.5%) patients met the primary endpoint (33 died and 45 had at least 1 HF hospitalization). In multivariate analysis, LVEF [Hazard Ratio (HR): 0.950; 95% Confidence Interval (CI): 0.917-0.983; p = 0.003), NYHA class I-II vs III-IV (HR: 0.480; CI: 0.272-0.842; p = 0.010), creatinine (HR: 2.653; CI: 1.548-4.545; p < 0.001) and pectoralis major area (HR: 0.873; 95% CI: 0.821-0.929; p < 0.001) were independent predictors of the primary endpoint, when adjusted for gender and NT-pro-BNP levels. Pectoralis major size measured by CMR in HFrEF was independently associated with a higher risk of death or HF hospitalization. Further studies to establish appropriate age and gender-adjusted cut-offs of muscle areas are needed to identify high-risk subgroups.
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