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NCT07047768
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.
NCT06632275
This mixed methods study aims to understand family care participation in the adult medicine wards of Chattogram Medical College Hospital, Bangladesh. The main questions it aims to answer, from the perspective of the patient, family caregiver, nurse, doctor, ward assistant, and hospital administrator, are: 1. What is the role of the family caregiver in hospital care? 2. What is the perceived effect of family participation in hospital care? 3. What are the barriers and facilitators experienced in family participation? 4. What are suggestions for family participation interventions? These questions will be answered with three study arms: 1. A prospective observational cohort (population: patients and family caregivers) 2. A time and motion study (population: nurses and doctors) 3. Interviews and focus group discussions (population: patients, family caregivers, nurses, doctors, ward assistants, and hospital administrators)
NCT06503822
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.
NCT06403826
The goal of this monocentric observational study involving acute hospitalised patients is to develop a classification algorithm for the detection of various movements parameters.
NCT04401150
LOVIT-COVID is a multicentre concealed-allocation parallel-group blinded randomized controlled trial to ascertain the effect of high-dose intravenous vitamin C compared to placebo on mortality or persistent organ dysfunction at 28 days in hospitalized COVID-19 patients.
NCT02119000
Bowel preparation is a crucial step prior to colonoscopy to help with the optimal assessment of the colonic mucosa. Inadequate bowel preparation increases the length of the procedure, and is associated with decreased lesional detection rates. The ideal bowel preparation formulation should be able to completely clean the bowel, without leaving solid or liquid residues, and without modifying the mucosal appearance. Bowel preparation may be administered in hospitalised patients or in the ER. Patients have less control on their environment and the intake of the bowel preparation. For example, there may be a delay in pharmacy delivery or inadequate supervision by the treating personnel. Hospitalised patients have more comorbidities, are usually less autonomous and mobile - both can add to the barriers leading to an adequate bowel preparation. Multiple studies have identified hospitalization status as an independent risk factor for poor bowel preparation. The objective of this study is to access which bowel preparation regimen, between PEG 3350 with electrolytes 2L the day before and 2L the day of the colonoscopy vs bisacodyl + PEG 3350 with electrolytes 1L the day before and 1L the day of the colonoscopy, results in the cleanest bowel preparation in hospitalised patients.
NCT02959632
The purpose of this study is to evaluate the common symptoms leading to an Animal Assisted Therapy consult, and to measure its (AAT) influence on the symptoms and feelings of hospitalized patients.
NCT02081846
Home Nurse Visit post discharge.