Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
OBJECTIVES: Acute hospitalization may lead to a decrease in muscle measures, but limited studies are reporting on the changes after discharge. The aim of this study was to determine longitudinal changes in muscle mass, muscle strength, and physical performance in acutely hospitalized older adults from admission up to 3 months post-discharge.DESIGN: A prospective observational cohort study was conducted.SETTING AND PARTICIPANTS: This study included 401 participants aged ≥70 years who were acutely hospitalized in 6 hospitals. All variables were assessed at hospital admission, discharge, and 1 and 3 months post-discharge.METHODS: Muscle mass in kilograms was assessed by multifrequency Bio-electrical Impedance Analysis (MF-BIA) (Bodystat; Quadscan 4000) and muscle strength by handgrip strength (JAMAR). Chair stand and gait speed test were assessed as part of the Short Physical Performance Battery (SPPB). Norm values were based on the consensus statement of the European Working Group on Sarcopenia in Older People.RESULTS: A total of 343 acute hospitalized older adults were included in the analyses with a mean (SD) age of 79.3 (6.6) years, 49.3% were women. From admission up to 3 months post-discharge, muscle mass (-0.1 kg/m2; P = .03) decreased significantly and muscle strength (-0.5 kg; P = .08) decreased nonsignificantly. The chair stand (+0.7 points; P < .001) and gait speed test (+0.9 points; P < .001) improved significantly up to 3 months post-discharge. At 3 months post-discharge, 80%, 18%, and 43% of the older adults scored below the cutoff points for muscle mass, muscle strength, and physical performance, respectively.CONCLUSIONS AND IMPLICATIONS: Physical performance improved during and after acute hospitalization, although muscle mass decreased, and muscle strength did not change. At 3 months post-discharge, muscle mass, muscle strength, and physical performance did not reach normative levels on a population level. Further research is needed to examine the role of exercise interventions for improving muscle measures and physical performance after hospitalization.
