View clinical trials related to Disease-related Malnutrition.
Filter by:Disease-related malnutrition (DRM) is a frequent syndrome in clinical practice, in which the mutual relationship between disease and malnutrition is observed. Inflammation, anorexia, changes in body composition or in energy and protein requirements, contribute to the development of DRM. The Global Leadership Initiative on Malnutrition (GLIM criteria) provides a diagnostic system of malnutrition that has been accepted by the main international scientific societies in the field of clinical nutrition. The GLIM criteria proposes an algorithm that includes phenotypic criteria (weight loss, underweight and low muscle mass), with their corresponding severity thresholds, and aetiological criteria (decreased oral intake, nutrient malabsorption and the presence of an inflammatory component). The diagnosis of malnutrition is established when an aetiological and a phenotypic criterion are met. The aim of the study is to determine the diagnostic and prognostic value of aetiological factors of malnutrition based on GLIM criteria, presence and degree of inflammation and dietary intake, in patients diagnosed with DRM.
The prevalence of malnutrition is estimated at 30-50% of hospitalized patients in China. Disease-related malnutrition increases the risk of infection, mortality, length of hospitalization as well as the economic burden. National Nutrition Plan proposed to reduce malnutrition, but a clear, effective roadmap and protocol has not existed yet. Several factors impede to resolve the above challenges. They include :1) the low efficiency of current malnutrition diagnosis methods; 2) the lack of dynamic, standard method that can evaluate nutritional status in quantitative way. To this end, the investigators aim to establish an artificial-intelligence malnutrition diagnosis system to improve the application of malnutrition Clinical Pathway. Firstly, the investigators will establish a multidimensional malnutrition large data set, based on our previously built national hospital nutrition screening data set. It will contain deep 3D facial images, semi-structured and structured electronic medical record. Then, the investigators will use ensemble learning algorithm to establish a fully automatic, artificial-intelligence malnutrition diagnosis model that includes both etiological and phenotypic diagnosis.