Clinical Research Directory

Virtual Reality in Intensive Care Unit: Patient Satisfaction, Clinical and Functional Outcomes, Feasibility

Background: Virtual reality is recognized as a progressive technology with the potential to enhance the rehabilitation process. Immersive virtual reality-based treatments can improve motor outcomes in physical rehabilitation, reduce stress caused by the intensive care unit environment and maximize patients' recovery processes. Objective: To analyze the efficacy of rehabilitation using immersive virtual reality on patient satisfaction and experience, functional and clinical outcomes and the feasibility of its application during the hospitalization period in an intensive care unit (ICU). Methods: This is a randomized controlled clinical trial involving ICU patients aged 18 years or older, with an estimated ICU stay of ≥ 72 hours, the ability to understand and follow verbal instructions and no diagnosis of psychotic disorders, visual impairment, or hearing loss. Patients will be divided into two groups: one undergoing traditional physiotherapy rehabilitation - control group (CG), and the other receiving physiotherapy using immersive virtual reality (IVR group). The following evaluations will be conducted: mobility condition, physical and psychological discomfort, estimated physical activity level, occurrence of delirium, cognitive decline screening, functionality classification, motivation level, patient experience, sense of immersion in the virtual reality environment, patient satisfaction during the rehabilitation process, occurrence of adverse events, and protocol feasibility. Data will be presented in graphs and tables. Results will be considered statistically significant at a 5% significance level (p ≤ 0.05), and all analyses will be performed using SPSS Statistics version 22. Expected results: The investigators expect to find evidence that rehabilitation using immersive virtual reality can promote and enable a more pleasant and less traumatic experience during the patient's hospitalization, with greater satisfaction and adherence to the proposed rehabilitation, in addition to being an effective tool to optimize patients' functional and clinical outcomes.

Cultural Adaptation, and Validation of Fundamentals of Care PRO-tool

A research team from the University of Seville and Flinders University has created a new 41-item questionnaire to assess fundamental nursing care. This questionnaire focuses on three main areas: the nurse-patient relationship, addressing patients' basic care needs (physical, relational, and psychosocial), and the context of care. This tool helps nurses evaluate and track the quality of care they provide over time. The questionnaire is a self-administered, patient-reported tool. The questionnaire has been translated into English and adapted for use in Australia, but not yet in Danish. Senior Researcher Maria Kjøller Pedersen from Copenhagen University Hospital, North Zealand is leading the effort to translate and adapt it for Denmark in collaboration with University College Copenhagen. The goal of this study is to translate, adapt, and validate the questionnaire for use in the Danish healthcare system.

Artificial Intelligence for Respiratory Infections SEverity Prediction

Health Data Warehouses (HDWs) are a major resource for the development of artificial intelligence (AI) applied to predictive and personalized medicine. We propose a project leveraging the HDW of the Hospices Civils de Lyon (HCL) to study acute lower respiratory tract infections (ALRTIs), a major public health issue due to their impact on morbidity, mortality, and healthcare costs. The COVID-19 pandemic has further highlighted their burden and complexity. ALRTIs can be caused by viral agents (e.g., influenza, RSV, SARS-CoV-2) or bacterial pathogens (e.g., pneumococcus, mycoplasma, legionella), and may be acquired in the community or during hospitalization. Given their frequency and potential severity, early identification of patients at risk of clinical deterioration is crucial, especially those likely to require intensive care. The recent deployment of the HCL HDW now allows for the structured extraction, linkage, and storage of administrative, clinical, biological, and pharmaceutical data. This system supports the reconstruction of each patient's care trajectory and clinical history, offering new opportunities for advanced modeling. In recent years, several predictive tools have been developed to estimate the severity or prognosis of respiratory infections, including PSI/FINE, qSOFA, CURB-65, the EPIC sepsis model, and early warning systems (EWS). The COVID-19 crisis spurred the creation of new scores and models to predict clinical outcomes or mortality, as well as online tools and apps for clinicians. However, many of these tools rely on limited datasets (often single-center or small cohorts), static variables (e.g., comorbidities), and do not consider the temporal dynamics of patient data. Some research teams have explored the use of multicenter data and machine learning (e.g., MLHO-Machine Learning to predict Health Outcomes), notably to model COVID-19 outcomes. Nonetheless, most models lack integration of longitudinal clinical and biological data, and few are generalizable to all respiratory infections. Additionally, existing tools rarely account for real-time contextual variables such as current levels of population immunity or vaccine availability. Our project aims to develop a dynamic AI-based detection algorithm to predict the risk of ICU admission in patients with ALRTIs. The model will be trained on retrospective HDW data from the HCL, including the evolution of vital signs, laboratory values, treatments, and demographic factors. By capturing temporal trends and clinical trajectories, our algorithm will go beyond static scoring systems and offer real-time risk stratification. Ultimately, this algorithm could be embedded in hospital information systems as a clinical decision support tool. By generating alerts for early signs of deterioration, it would enable more timely interventions, resource optimization, and improved patient outcomes. This approach differs from existing models in two fundamental ways. First, it covers a broad patient population with viral and bacterial pneumonia of both community and hospital origin. Second, it explicitly incorporates the longitudinal dimension of health data, allowing the model to learn from dynamic changes in patient condition. This temporal perspective is key to improving prediction accuracy and enabling early detection of deterioration.

Comparison of Application Effects Between Long- and Standard Short- Peripheral Venous Catheters

Short PIVC (intravenous indentation needle) accounts for more than 50% of clinical infusion tools, but long PIVC is rarely used and studied in China. This study aims to explore the application characteristics and application effects of long PIVC in China. It provides reference for the correct selection of infusion tools, and promotes the clinical application and promotion of new intravenous therapy tools. The study nurse will work with the responsible physician to assess the eligibility for enrollment and sign the informed consent. Were randomly assigned to the control group (to receive a new 24G/22G (0.7mm\*19mm/0.9mm\*25mm) short PIVC (closed needle protected venous catheter system) puncture) or the intervention group (to insert a new 3F (8cm) or 4F(10cm) long PIVC) for daily routine maintenance until catheter removal, General demographic data, laboratory-related data, catheter-related data, catheter-related complications (unplanned extubation, phlebitis, catheter blockage, catheter-related thrombosis, catheter-related bloodstream infection, exudation, etc.) and patient satisfaction were collected.