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.
Background: Our aim was to identify dietary patterns by the level of maternal education that contribute to BMI, fat mass index (FMI), and fat-free mass index (FFMI) in children at age 5 and to assess if these dietary patterns are related to BMI at age 10. Methods: Per group (low/middle/high level), Reduced Rank Regression (RRR) was used to derive dietary patterns for the response variables BMI z-score, FMI, and FFMI in 1728 children at age 5 in the Amsterdam Born Children and their Development (ABCD) cohort. Regression analyses were then used to determine the association with BMI at age 10. Results: In each group, pattern 1 was characterized by its own cluster of food groups. Low: water/tea, savory snacks, sugar, low-fat meat, and fruits; middle: water/tea, low-fat cheese, fish, low-fat dairy, fruit drink, low-fat meat, and eggs; and high: low-fat cheese, fruits, whole-grain breakfast products, and low-fat and processed meat. Additionally, in each group, pattern 1 was positively associated with BMI z-scores at age 10 (low: β ≤ 0.43 [95% CI ≤ 0.21; 0.66], p < 0.001, middle: β ≤ 0.23 [0.09; 0.36], p ≤ 0.001, and high: β ≤ 0.24 [0.18; 0.30], p < 0.001). Conclusions: The dietary patterns stratified by the level of maternal education are characterized by different food groups. But in all the groups, pattern 1 is positively associated with BMI at age 10.
MULTIFILE
Background & aims: In dietary practice, it is common to estimate protein requirements on actual bodyweight, but corrected bodyweight (in cases with BMI <20 kg/m2 and BMI ≥30 kg/m2) and fat free mass (FFM) are also used. Large differences on individual level are noticed in protein requirements using these different approaches. To continue this discussion, the answer is sought in a large population to the following question: Will choosing actual bodyweight, corrected bodyweight or FFM to calculate protein requirements result in clinically relevant differences? Methods: This retrospective database study, used data from healthy persons ≥55 years of age and in- and outpatients ≥18 years of age. FFM was measured by air displacement plethysmography technology or bioelectrical impedance analysis. Protein requirements were calculated as 1) 1.2 g (g) per kilogram (kg) actual bodyweight or 2) corrected bodyweight or 3) 1.5 g per kg FFM. To compare these three approaches, the approach in which protein requirement is based on FFM, was used as reference method. Bland–Altman plots with limits of agreement were used to determine differences, analyses were performed for both populations separately and stratified by BMI category and gender. Results: In total 2291 subjects were included. In the population with relatively healthy persons (n = 506, ≥55 years of age) mean weight is 86.5 ± 18.2 kg, FFM is 51 ± 12 kg and in the population with adult in- and outpatients (n = 1785, ≥18 years of age) mean weight is 72.5 ± 18.4 kg, FFM is 51 ± 11 kg. Clinically relevant differences were found in protein requirement between actual bodyweight and FFM in most of the participants with overweight, obesity or severe obesity (78–100%). Using corrected bodyweight, an overestimation in 48–92% of the participants with underweight, healthy weight and overweight is found. Only in the Amsterdam UMC population, protein requirement is underestimated when using the approach of corrected bodyweight in participants with severe obesity. Conclusion: The three approaches in estimation of protein requirement show large differences. In the majority of the population protein requirement based on FFM is lower compared to actual or corrected bodyweight. Correction of bodyweight reduces the differences, but remain unacceptably large. It is yet unknown which method is the best for estimation of protein requirement. Since differences vary by gender due to differences in body composition, it seems more accurate to estimate protein requirement based on FFM. Therefore, we would like to advocate for more frequent measurement of FFM to determine protein requirements, especially when a deviating body composition is to be expected, for instance in elderly and persons with overweight, obesity or severe obesity.
