At the beginning of the twenty first century obesity entered Dutch maternity care as a ‘new illness’ challenging maternity care professionals in providing optimal care for women with higher BMI’s. International research revealed that obese women had more perinatal problems than normal weight women. However, the effect of higher BMIs on perinatal outcomes had never been studied in women eligible for midwife-led primary care at the outset of their pregnancy. In the context of the Dutch maternity care system, it was not clear if obesity should be treated as a high-risk situation always requiring obstetrician-led care or as a condition that may lead to problems that could be detected in a timely manner in midwife-led care using the usual risk assessment tools. With the increased attention on obesity in maternity care there was also increased interest in GWG. Regarding GWG in the Netherlands, the effect of insufficient or excessive GWG on perinatal outcomes had never been studied and there were no validated guidelines for GWG. A midwife’s care for the individual woman in the context of the Dutch maternity care system - characterised by ‘midwife-led care if possible, obstetrician-led care if needed’ - is hampered by the lack of national multidisciplinary consensus regarding obesity and weight gain. Obesity has not yet been included in the OIL and local protocols contain varying recommendations. To enable sound clinical decisions and to offer optimal individual care for pregnant women in the Netherlands more insights in weight and weight gain in relation to perinatal outcomes are required. With this thesis the author intends to contribute to the body of knowledge on weight and weight gain to enhance optimal midwife-led primary care for the individual woman and to guide midwives’ clinical decision-making.
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
