Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
BACKGROUND: Generalized Joint Hypermobility (GJH) has been found to be associated with musculoskeletal complaints and disability. For others GJH is seen as a prerequisite in order to excel in certain sports like dance. However, it remains unclear what the role is of GJH in human performance. Therefore, the purpose of the study was to establish the association between GJH and functional status and to explore the contribution of physical fitness and musculoskeletal complaints to this association.METHODS: A total of 72 female participants (mean age (SD; range): 19.6 (2.2; 17-24)) were recruited among students from the Amsterdam School of Health Professions (ASHP) (n = 36) and the Amsterdam School of Arts (ASA), Academy for dance and theater (n = 36) in Amsterdam, The Netherlands. From each participant the following data was collected: Functional status performance (self-reported Physical activity level) and capacity (walking distance and jumping capacity: side hop (SH) and square hop (SQH)), presence of GJH (Beighton score ≥4), muscle strength, musculoskeletal complaints (pain and fatigue) and demographic characteristics (age and BMI).RESULTS: GJH was negatively associated with all capacity measures of functional status. Subjects with GJH had a reduced walking distance (B(SE):-75.5(10.5), p = <.0001) and jumping capacity (SH: B(SE):-10.10(5.0), p = .048, and SQH: B(SE):-11.2(5.1), p = .024) in comparison to subjects without GJH, when controlling for confounding: age, BMI and musculoskeletal complaints. In participants with GJH, functional status was not associated with performance measures.CONCLUSION: GJH was independently associated with lower walking and jumping capacity, potentially due to the compromised structural integrity of connective tissue. However, pain, fatigue and muscle strength were also important contributors to functional status.
Generalized joint hypermobility (GJH) is highly prevalent among patients diagnosed with chronic pain. When GJH is accompanied by pain in ≥4 joints over a period ≥3 months in the absence of other conditions that cause chronic pain, the hypermobility syndrome (HMS) may be diagnosed. In addition, GJH is also a clinical sign that is frequently present in hereditary diseases of the connective tissue, such as the Marfan syndrome, osteogenesis imperfecta, and the Ehlers-Danlos syndrome. However, within the Ehlers-Danlos spectrum, a similar subcategory of patients having similar clinical features as HMS but lacking a specific genetic profile was identified: Ehlers-Danlos syndrome hypermobility type (EDS-HT). Researchers and clinicians have struggled for decades with the highly diverse clinical presentation within the HMS and EDS-HT phenotypes (Challenge 1) and the lack of understanding of the pathological mechanisms that underlie the development of pain and its persistence (Challenge 2). In addition, within the HMS/EDS-HT phenotype, there is a high prevalence of psychosocial factors, which again presents a difficult issue that needs to be addressed (Challenge 3). Despite recent scientific advances, many obstacles for clinical care and research still remain. To gain further insight into the phenotype of HMS/EDS-HT and its mechanisms, clearer descriptions of these populations should be made available. Future research and clinical care should revise and create consensus on the diagnostic criteria for HMS/EDS-HT (Solution 1), account for clinical heterogeneity by the classification of subtypes within the HMS/EDS-HT spectrum (Solution 2), and create a clinical core set (Solution 3).
