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Sarcopenia Assessment Using Fully Automated Deep Learning Predicts Cardiac Allograft Survival in Heart Transplant
Frederick M Lang1, Jianfei Liu2, Kevin J Clerkin3
1Department of Medicine, Massachusetts General Hospital, Boston (F.M.L.).
Background:
Sarcopenia is associated with adverse outcomes in patients with end-stage heart failure. Muscle mass can be quantified via manual segmentation of computed tomography images, but this approach is time-consuming and subject to interobserver variability. We sought to determine whether fully automated assessment of radiographic sarcopenia by deep learning would predict heart transplantation outcomes.
Methods:
This retrospective study included 164 adult patients who underwent heart transplantation between January 2013 and December 2022. A deep learning-based tool was utilized to automatically calculate cross-sectional skeletal muscle area at the T11, T12, and L1 levels on chest computed tomography. Radiographic sarcopenia was defined as skeletal muscle index (skeletal muscle area divided by height squared) in the lowest sex-specific quartile.
Results:
The study population had a mean age of 53±14 years and was predominantly men (75%) with a nonischemic cause of cardiomyopathy (73%). Mean skeletal muscle index was 28.3±7.6 cm2/m2 for women versus 33.1±8.1 cm2/m2 for men (P<0.001). Cardiac allograft survival was significantly lower in heart transplant recipients with versus without radiographic sarcopenia at T11 (90% versus 98% at 1 year, 83% versus 97% at 3 years, log-rank P=0.02). After multivariable adjustment, radiographic sarcopenia at T11 was associated with an increased risk of cardiac allograft loss or death (hazard ratio, 3.86 [95% CI, 1.35-11.0]; P=0.01). Patients with radiographic sarcopenia also had a significantly increased hospital length of stay (28 [interquartile range, 19-33] versus 20 [interquartile range, 16-31] days; P=0.046).
Conclusions:
Fully automated quantification of radiographic sarcopenia using pretransplant chest computed tomography successfully predicts cardiac allograft survival. By avoiding interobserver variability and accelerating computation, this approach has the potential to improve candidate selection and outcomes in heart transplantation.
