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.
BackgroundICU patients lose muscle mass rapidly and maintenance of muscle mass may contribute to improved survival rates and quality of life. Protein provision may be beneficial for preservation of muscle mass and other clinical outcomes, including survival. Current protein recommendations are expert-based and range from 1.2 to 2.0 g/kg. Thus, we performed a systematic review and meta-analysis on protein provision and all clinically relevant outcomes recorded in the available literature.MethodsWe conducted a systematic review and meta-analyses, including studies of all designs except case control and case studies, with patients aged ≥18 years with an ICU stay of ≥2 days and a mean protein provision group of ≥1.2 g/kg as compared to <1.2 g/kg with a difference of ≥0.2 g/kg between protein provision groups. All clinically relevant outcomes were studied. Meta-analyses were performed for all clinically relevant outcomes that were recorded in ≥3 included studies.ResultsA total of 29 studies published between 2012 and 2022 were included. Outcomes reported in the included studies were ICU, hospital, 28-day, 30-day, 42-day, 60-day, 90-day and 6-month mortality, ICU and hospital length of stay, duration of mechanical ventilation, vomiting, diarrhea, gastric residual volume, pneumonia, overall infections, nitrogen balance, changes in muscle mass, destination at hospital discharge, physical performance and psychological status. Meta-analyses showed differences between groups in favour of high protein provision for 60-day mortality, nitrogen balance and changes in muscle mass.ConclusionHigh protein provision of more than 1.2 g/kg in critically ill patients seemed to improve nitrogen balance and changes in muscle mass on the short-term and likely 60-day mortality. Data on long-term effects on quality of life are urgently needed.
MULTIFILE
Skeletal muscle-related symptoms are common in both acute coronavirus disease (Covid)-19 and post-acute sequelae of Covid-19 (PASC). In this narrative review, we discuss cellular and molecular pathways that are affected and consider these in regard to skeletal muscle involvement in other conditions, such as acute respiratory distress syndrome, critical illness myopathy, and post-viral fatigue syndrome. Patients with severe Covid-19 and PASC suffer from skeletal muscle weakness and exercise intolerance. Histological sections present muscle fibre atrophy, metabolic alterations, and immune cell infiltration. Contributing factors to weakness and fatigue in patients with severe Covid-19 include systemic inflammation, disuse, hypoxaemia, and malnutrition. These factors also contribute to post-intensive care unit (ICU) syndrome and ICU-acquired weakness and likely explain a substantial part of Covid-19-acquired weakness. The skeletal muscle weakness and exercise intolerance associated with PASC are more obscure. Direct severe acute respiratory syndrome coronavirus (SARS-CoV)-2 viral infiltration into skeletal muscle or an aberrant immune system likely contribute. Similarities between skeletal muscle alterations in PASC and chronic fatigue syndrome deserve further study. Both SARS-CoV-2-specific factors and generic consequences of acute disease likely underlie the observed skeletal muscle alterations in both acute Covid-19 and PASC.
