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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Background: Mechanically ventilated patients are at risk of developing inspiratory muscle weakness (IMW), which is associated with failure to wean and poor outcomes. Inspiratory muscle training (IMT) is a recommended intervention during and after extubation but has not been widely adopted in Dutch intensive care units (ICUs). Objectives: The objective of this study was to explore the potential, barriers, and facilitators for implementing IMT as treatment modality for mechanically ventilated patients. Methods: This mixed-method, proof-of-concept study was conducted in a large academic hospital in the Netherlands. An evidence-based protocol for assessing IMW and training was applied to patients ventilated for ≥24 h in the ICU during an 8-month period in 2021. Quantitative data on completed measurements and interventions during and after ICU-stay were collected retrospectively and were analysed descriptively. Qualitative data were collected through semistructured interviews with physiotherapists executing the new protocol. Interview data were transcribed and thematically analysed. Findings: Of the 301 screened patients, 11.6% (n = 35) met the inclusion criteria. Measurements were possible in 94.3% of the participants, and IMW was found in 78.8% of the participants. Ninety-six percent started training in the ICU, and 88.5% continued training after transfer to the ward. Follow-up measurements were achieved in 73.1% of the patients with respiratory muscle weakness. Twelve therapists were interviewed, of whom 41.7% regularly worked in the ICU. When exploring reasons for protocol deviation, three themes emerged: “professional barriers”, “external factors”, and “patient barriers”. Conclusions: Implementation of measurements of and interventions for IMW showed to be challenging in this single centre study. Clinicians' willingness to change their handling was related to beliefs regarding usefulness, effectiveness, and availability of time and material. We recommend that hospitals aiming to implement IMT during or after ventilator weaning consider these professional and organisational barriers for implementation of novel, evidence-based interventions into daily clinical practice.
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