Renal Diseases

The goal of this clinical trial is to learn whether a new way of organising hospital nutrition care can improve outcomes for adults who are admitted to medical wards and are at risk of malnutrition. The main questions it aims to answer are: * Does having clinical dietitians take primary responsibility for nutrition care shorten hospital stay? * Does this approach improve nutritional intake, nutritional status, quality of life, and patient satisfaction? * Does this approach lower healthcare costs compared with current practice? Researchers will compare the new nutrition care model with current practice. In current practice, nursing staff are mainly responsible for nutrition screening and nutrition care planning, while clinical dietitians are involved only when requested. In the new model, clinical dietitians are part of the ward team and take primary responsibility for identifying patients at nutritional risk, planning nutritional care, and providing nutritional treatment. Participants will: * Receive either the current nutrition care approach or the new dietitian-led nutrition care approach, depending on when they are admitted to the ward * Complete questionnaires about their nutritional status, quality of life, and daily activities during their hospital stay and about 30 days later * Have information collected during their hospital stay and from their medical records, including nutritional intake, body weight, length of hospital stay, hospital readmissions, and other health outcomes The study will be conducted on three medical wards at Gødstrup Hospital. All wards will start with current practice and will gradually change to the new nutrition care model during the study period. Researchers will compare outcomes before and after the new model is introduced.

SMART DIALYSIS - Scaling Machine Learning and Artificial Intelligence AlgoRithms to OpTimize the Performance and Delivery of Acute DIALYSIS. Hypothesis: Can the investigators develop and implement Machine Learning and Artificial Intelligence Algorithms into Clinical Information Systems to Optimize the Prescription, Delivery, and Performance of Acute Dialysis? Objective(s): 1. Identify variables surrounding identified Key Performance Indicators that may be used by Machine Learning and Artificial Intelligence algorithms to optimize the prescription and performance of acute dialysis. 2. Develop Machine Learning and Artificial Intelligence algorithms to help guide the prescription and delivery of acute dialysis in the development of Clinical Decision Support tools and Best Practice Advisories and create a ML/AI Augmented SMART DIALYSIS Digital Dashboard. 3. Implement and evaluate the performance of the developed Machine Learning and Artificial Intelligence algorithms on patient-centered and health economic outcomes. 4. Validate and benchmark the performance of the evaluated Machine Learning and Artificial Intelligence algorithms across multiple jurisdictions.

This is a single-center observational study aimed at setting up and testing new ultrasound vascular imaging protocols, conducted exclusively for research purposes. The study will perform US examinations in 60 subjects. The subjects enrolled in the study will be examined the first time and will then provide consent to be examined again in the future if needed. The primary aim of this study is to set-up and test new advanced US protocol for the arm and cerebral blood vessels The secondary objectives will be: * Define ranges of normality/reference values of US-based parameters to be compared with pathological values. * Evaluate the repeatability and reproducibility of the acquired US measurements. * Evaluate the correlation between age and the acquired US measurements. * Evaluate the correlation between gender and the acquired US measurements.

The project aims to study the impact of SGLT2i therapy on clinical and biochemical aspects. in elderly. The project will combine different types of data, such as clinical and biological information, and will use advanced Bayesian statistical methods to understand the relationship between risk factors and patient outcomes following treatment with SGLT2 inhibitors.