Nutrition
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This study is an assessment of home parenteral nutrition service performance and safety and efficacy outcomes in patients with benign chronic intestinal failure. ⋯ This study confirms the importance of setting up and maintaining structured registries to monitor and improve home parenteral nutrition care. Safety outcomes have improved over the years, most likely due to the underlying efficient nursing service.
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Multicenter Study
Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study.
This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer. ⋯ Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.
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Multicenter Study
Myosteatosis predicts short-term mortality in patients with COVID-19: A multicenter analysis.
Body composition on computed tomography can predict prognosis in patients with COVID-19. The reported data are based on small retrospective studies. The aim of the present study was to analyze the prognostic relevance of skeletal muscle parameter derived from chest computed tomography for prediction of 30-d mortality in patients with COVID-19 in a multicenter setting. ⋯ Myosteatosis is strongly associated with 30-d mortality in patients COVID-19. Patients with COVID-19 with myosteatosis should be considered a risk group.
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To assess the concurrent and predictive validity of different combinations of Global Leadership Initiative on Malnutrition (GLIM) criteria in patients with colorectal cancer considering different indicators of reduced muscle mass (MM) and the effects of the disease. ⋯ Satisfactory concurrent validity was not verified. The GLIM diagnosis of malnutrition was associated with postoperative complications and mortality.
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Multicenter Study Observational Study
Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm.
Cancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort. ⋯ Machine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.