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.
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BACKGROUND: Muscle quantity at intensive care unit (ICU) admission has been independently associated with mortality. In addition to quantity, muscle quality may be important for survival. Muscle quality is influenced by fatty infiltration or myosteatosis, which can be assessed on computed tomography (CT) scans by analysing skeletal muscle density (SMD) and the amount of intermuscular adipose tissue (IMAT). We investigated whether CT-derived low skeletal muscle quality at ICU admission is independently associated with 6-month mortality and other clinical outcomes.METHODS: This retrospective study included 491 mechanically ventilated critically ill adult patients with a CT scan of the abdomen made 1 day before to 4 days after ICU admission. Cox regression analysis was used to determine the association between SMD or IMAT and 6-month mortality, with adjustments for Acute Physiological, Age, and Chronic Health Evaluation (APACHE) II score, body mass index (BMI), and skeletal muscle area. Logistic and linear regression analyses were used for other clinical outcomes.RESULTS: Mean APACHE II score was 24 ± 8 and 6-month mortality was 35.6%. Non-survivors had a lower SMD (25.1 vs. 31.4 Hounsfield Units (HU); p < 0.001), and more IMAT (17.1 vs. 13.3 cm(2); p = 0.004). Higher SMD was associated with a lower 6-month mortality (hazard ratio (HR) per 10 HU, 0.640; 95% confidence interval (CI), 0.552-0.742; p < 0.001), and also after correction for APACHE II score, BMI, and skeletal muscle area (HR, 0.774; 95% CI, 0.643-0.931; p = 0.006). Higher IMAT was not significantly associated with higher 6-month mortality after adjustment for confounders. A 10 HU increase in SMD was associated with a 14% shorter hospital length of stay.CONCLUSIONS: Low skeletal muscle quality at ICU admission, as assessed by CT-derived skeletal muscle density, is independently associated with higher 6-month mortality in mechanically ventilated patients. Thus, muscle quality as well as muscle quantity are prognostic factors in the ICU.TRIAL REGISTRATION: Retrospectively registered (initial release on 06/23/2016) at ClinicalTrials.gov: NCT02817646 .
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The world population is ageing rapidly. As society ages, the incidence of physical limitations is dramatically increasing, which reduces the quality of life and increases healthcare expenditures. In western society, ~30% of the population over 55 years is confronted with moderate or severe physical limitations. These physical limitations increase the risk of falls, institutionalization, co-morbidity, and premature death. An important cause of physical limitations is the age-related loss of skeletal muscle mass, also referred to as sarcopenia. Emerging evidence, however, clearly shows that the decline in skeletal muscle mass is not the sole contributor to the decline in physical performance. For instance, the loss of muscle strength is also a strong contributor to reduced physical performance in the elderly. In addition, there is ample data to suggest that motor coordination, excitation-contraction coupling, skeletal integrity, and other factors related to the nervous, muscular, and skeletal systems are critically important for physical performance in the elderly. To better understand the loss of skeletal muscle performance with ageing, we aim to provide a broad overview on the underlying mechanisms associated with elderly skeletal muscle performance. We start with a system level discussion and continue with a discussion on the influence of lifestyle, biological, and psychosocial factors on elderly skeletal muscle performance. Developing a broad understanding of the many factors affecting elderly skeletal muscle performance has major implications for scientists, clinicians, and health professionals who are developing therapeutic interventions aiming to enhance muscle function and/or prevent mobility and physical limitations and, as such, support healthy ageing.
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