Clinical Research Directory

The Effect of Changes in the Frequency of Endotracheal Tube Repositioning in Intensive Care Units on the Prevention of Oral Mucosal Pressure Injuries

Intensive care units (ICUs) are care centers equipped with a wide range of technological tools and devices used to provide the highest level of care to individuals whose lives are at risk, requiring a multidisciplinary team approach. Patients may require respiratory support from a ventilator to maintain breathing. In the intensive care unit, patients' breathing is maintained using a medical device called an endotracheal tube (ETT). ETTs can cause injuries due to the constant pressure they exert on the patient's mouth and lip area. Therefore, nurses must secure the ETT at specific times to different points in the mouth area (right and left sides of the mouth) to prevent these injuries. Injuries to the mucosa caused by medical equipment are defined as "mucosal membrane pressure injuries (MMPI)." One such injury is an injury to the mouth. Injuries occurring in the mouth are caused by the pressure of the tubes that enable the patient to breathe in and out. "The Effect of Changes in the Frequency of Endotracheal Tube Repositioning in Intensive Care Units on the Prevention of Oral Mucosal Pressure Injuries" is a doctoral thesis study; the aim of this study is to investigate the effect of differences in the time interval for changing the position of the tube in the lip region on the prevention of injuries occurring in the mouth in patients monitored with ETT in the ICU. A review of the literature revealed that there are gaps in studies conducted at the international or national level on this subject, and that there is no definitive time guideline for changing (repositioning) the tube in the lip area. The study will include patients over the age of 18 who are being monitored with an ETT in their mouth, who did not have any injuries in the mouth area at the time of admission to the ICU, who have not had the tube in their mouth for more than 24 hours, who are in moderate to good general condition, and whose first-degree relatives or legal presentative have given permission for them to participate in the study. In the study, the follow-up and evaluation of patients will be limited to fourteen days after tube insertion. Exclusion Criteria; Differences in respiratory support, Pre-existing artificial airway, Oncological conditions, Neurological and behavioral factors, Positional constraints, Sensory/perceptual impairment, Pre-existing injury. Patients who are discharged or transferred during the fourteen-day follow-up period will be excluded from the study. According to the sample calculation for the study, a total of 230 patients (115 volunteers in the 4-hour group and 115 volunteers in the 8-hour group) will be sufficient. In the study, patients will be stratified according to their APACHE II scores. ICU nurses are not bound by any specific time for securing the ETT. Nurses positioned the tube fixation site at the right lip edge/left lip edge/mid-lip line; however, since they were not bound by a specific time-based rule, tube care was nurse-specific. In this study, the location of tube fixation will be recorded through observation to determine the effect of tube fixation on the formation of oral injuries. This study is planned to be completed within a 24-month timeframe between May 30, 2026, and June 15, 2027. This research is a scientific research (doctoral thesis) study. The daily tube care of volunteers is already in place, and we emphasize that this research will not affect/interfere with the treatment and follow-up of volunteers, and that volunteers will not be negatively affected by the study in any way. The principal investigator will randomly assign volunteers being monitored with ETT to groups using a computer system and will request nurses to reposition their ETTs based on the time intervals (4 hours-8 hours) within these groups. At the same time, the researcher will collect relevant clinical data that may affect pressure injuries in patients (age, gender, smoking history, body mass index, medical history, ICU admission diagnosis, SOFA, RASS and APACHE II scores, Glasgow Coma Scale, laboratory findings (C-reactive protein, white blood cell, Hemoglobin, Hematocrit, Total Protein, Albumin, pH, PO2, Blood Sugar Level), Nutritional Status (Route and Type of Nutrition, Product), Medication Support (Sedatives, Inotropes, Antibiotics, Steroids), ETT-Related Data (Number, Depth, Fixation Site, Repositioning Time), Eilers Oral Assessment Guide and Oral Care Frequency, Braden Pressure Ulcer Risk Assessment Scale and Overall Body Pressure Ulcer Presence, Reaper Oral Mucosa Pressure Injury Scale Grade data, etc.). The follow-up period in the study will be fourteen days, during which the principal investigator will record the patients' personal health data mentioned above and terminate the follow-up of patients who develop pressure injuries in the oral mucosa. Our expectation from the study is that no injuries will occur in the mouth. In the event of a possible injury, the intervention will be terminated immediately.

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.

Role of Elastic Power in Acute Respiratory Distress Syndrome

Mechanical ventilation is essential for managing acute respiratory distress syndrome (ARDS), but it can also cause ventilator-induced lung injury (VILI) due to mechanical forces. VILI results from the interaction between lung structure and mechanical ventilation factors, such as tidal volume, plateau pressure, driving pressure, inspiratory flow, respiratory rate, and PEEP. Intrinsic factors like lung heterogeneity further increase the risk. Elastic power (EP), a key component, is linked to repetitive alveolar stretching and disease progression. Study Objectives: Examine the correlation between elastic power and pulmonary hyperinflation. Compare EP's sensitivity and specificity with other overdistension markers like driving pressure, plateau pressure, upper inflection point, and compliance.