Active clinical trials for "Critical Illness"

Rationale: There is large heterogeneity in disease states of critically ill patients at ICU admittance and there is also large heterogeneity in their disease severity during ICU stay. Still, some patients may show remarkable similarities in disease patterns. There is a lack of understanding of causal mechanisms that lead to divergent outcomes in critically ill patients, and at the same time different diseases may share common underlying, yet unidentified, causal pathways that could explain similarities between different diseases. Objective: To explore the association between patient characteristics and the severity of organ failure in critically ill patients admitted to the ICU Study design: Prospective cohort study Study population: Adult critically ill patients in the ICU Intervention (if applicable): not applicable Main study parameters/endpoints: Maximum severity of organ failure observed during ICU stay measured by the maximum SOFA score and quality of life at one year follow-up

Intensive care patients are at high risk to develop deep venous thrombosis and pulmonary embolism. Despite anticoagulation with heparin 7% of ICU patients suffer from this serious complication. Optimal regimens for prevention of VTE have been established in medical patients only and are not known for ICU patients. It was therefore the aim of this study to compare the bioavailability of a low molecular weight heparin in ICU patients and in medical patients. Furthermore, we looked wether a 50% dose increase resulted in better bioavailability of this drug.

This is a prospective longitudinal study that will evaluate the unmet needs of older adults (65 and older) who return home (either directly or after short-term rehab) after an ICU hospitalization, evaluate the association of these unmet needs with clinically relevant outcomes, and assess barriers and facilitators to addressing these unmet needs. The proposed research will inform the development and evaluation of a subsequent intervention to improve functional outcomes among older ICU survivors, in alignment with the NIH's mission to reduce disability.

Weaning and extubation are essential steps for the management of critically ill patients when mechanical ventilation (MV) is no longer required. Extubation failure (EF) occurs in approximately 10-30% (1,2) of all patients meeting the readiness criteria and have tolerated a spontaneous breathing trial (SBT). EF is associated with prolonged MV, as well as increased morbidity and mortality (2). Therefore, the early identification of critically ill patients who are likely to experience EF is vital for improved outcomes. EF can result from different factors (respiratory, metabolic, neuromuscular), particularly cardiac factor, and can be caused by the inability of the respiratory muscle pump to tolerate increases in the cardiac and respiratory load (1,3). Respiratory drive represents the intensity of the neural stimulus to breathe. In mechanically ventilated patients, it can be abnormally low (i.e., suppressed or insufficient) or abnormally high (i.e., excessive), and thus result in excessively low or high inspiratory effort, leading to potential injury to the respiratory muscles (i.e., myotrauma) (4,5) or to the lungs. A high incidence of abnormal drive (low or high) may explain the high incidence of diaphragm dysfunction at time of separation from mechanical ventilation (6). Airway occlusion pressure (P0.1) is the drop in airway pressure (Paw) 100 milliseconds after the onset of inspiration during an end-expiratory occlusion of the airway (7). P0.1 measurement is not perceived by the patient and does not influence respiratory pattern. It is, in theory, a reliable measure of respiratory drive because the brevity of the occlusion explains that it is not affected by patient's response to the occlusion and it is independent of respiratory mechanics (8). P0.1 has also been correlated with inspiratory effort (9, 10) and it has been shown that in patients under assisted mechanical ventilation P0.1 might be able to detect potentially excessive inspiratory effort (11). P0.1 is a non-invasive measure and clinically available at bedside since currently nearly all modern ventilators provide a means of measuring it. Originally, a high P0.1 during a spontaneous breathing trial was associated with failure, suggesting that a high respiratory drive could predict weaning failure. However, only a few and old clinical studies investigated the association between P0.1 and extubation failure (EF) and were not conclusive (12,13). We hypothesized that patients with EF would have increased P0.1 values during spontaneous breathing trial (SBT). Therefore, the aims of our study will be to (1) to evaluate the ability of changes in P0.1 (Delta-P0.1) during SBT to predict EF and (2) to assess if Delta-P0.1 is an independent predictor of EF.

The purpose of this study is to determine if the use of ReliZORB improves nutrition tolerance and helps critically ill patients meet their nutrition goals. Subjects in the intensive care unit will be enrolled and randomized 2:1 to receive ReliZORB or placebo cartridges with enteral feedings for 5 days. Blood and stool samples will be collected to test for nutrition and inflammation.

We will show in this study the impact of use the rapid diagnostic method (multiplex PCR filmArray) on clinical and pharmacoeconomic aspects among Critically Ill Patients.

Advanced stages of the response to life-threatening infection, severe trauma, or other physiological insults often lead to exhaustion of the homeostatic mechanisms that sustain normal blood pressure and oxygenation. These syndromic presentations often meet the diagnostic criteria of sepsis and/or the acute respiratory distress syndrome (ARDS), the two most common syndromes encountered in the intensive care unit (ICU). Although critical illness syndromes, such as sepsis and ARDS, have separate clinical definitions, they often overlap clinically and share several common injury mechanisms. Moreover, there are no specific therapies for critically ill patients, and as a consequence, approximately 1 in 4 patients admitted to the ICU will not survive. The purpose of this observational study is to identify early patient biologic factors that are present at the time of ICU admission that will help diagnose critical illness syndromes earlier, identify who could benefit most from specific therapies, and enable the discovery of new treatments for syndromes such as sepsis and ARDS.

The purpose of our study is to assess lung aeration and diaphragmatic indices by transthoracic ultrasonography in patients ready to be weaned from mechanical ventilation as predictors of weaning success

The purpose of this study is to investigate discomforts experienced by intensive care patients during their critical illness period. We will use the Norwegian version of the questionnaire Inconforts des Patients de REAnimation (IPREA), the IPREA-N.Patients will be asked to rate18 questions about different possible discomforts on a 0-10 scale after their intensive care stay. Furthermore we aim to test whether the questionnaire when translated into Norwegian is useful in the Norwegian patient population. The aim of the study is to assess perceived discomfort in intensive care patients using the IPREA-N questionnaire to test psychometric properties of the questionnaire

Introduction: Early and rapid diagnosis of etiology is often an important part of saving the lives of patients in emergency department. Chest CT is an important examination method for emergency diagnosis because of its fast examination speed and accurate localization. Traditional medical imaging diagnosis relies on radiologists to report in a qualitative and subjective manner. Through the interdisciplinary combination of clinical, imaging and artificial intelligence, the integration of multi-omics data, the construction of large-scale language models, and the construction of the auxiliary diagnosis support system of "one check for multiple diseases" provide new ideas and means for the rapid and accurate screening of emergency critical diseases. Method： Study design Investigators retrospectively collected cardiovascular, respiratory, digestive, and neurological CT images, demographic data, medical history and laboratory date of emergency department patients during the period from 1 January 2018 and 30 December 2024. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department.The inclusion criteria are:1. adult emergency patients with cardiovascular, respiratory, digestive, and nervous system diseases; 2. These patients had CT images. Patients with incomplete clinical or radiographic data were excluded from the analysis. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department. Based on the collected medical text data, an artificial intelligence large-scale language model algorithm framework is built. After the structure annotation of chest CT images is performed by doctors above the intermediate level of imaging, the Transformer deep neural network is trained for CT image segmentation, and a series of tasks such as structural structure segmentation, damage detection, disease classification and automatic report generation are developed based on Vision Transformer self-attention architecture mechanism. A multi-disease diagnosis and treatment decision-making system based on chest CT images, clinical text and examination multimodal data was constructed and validated. Disscusion Emergency medicine deals mainly with unpredictable critical and sudden illnesses. Patients who come to the emergency department for medical treatment often have acute onset, hidden condition, rapid progress, many complications, high mortality and disability rate. Assisted diagnosis systems developed by combining clinical text, images and artificial intelligence can greatly improve the ability of emergency department doctors to accurately diagnose diseases. This study fills the blank of CT artificial intelligence aided diagnosis system for emergency patients, and provides a rapid diagnosis scheme for multi-system and multi-disease. Finally, the results will be transformed into clinical application software and used and promoted in clinical work to improve the diagnosis and treatment level.