Active clinical trials for "Emergencies"

The main objective of our study is to determine the diagnostic performance of an ultra-portable ultrasound (V-Scan®) device for emergency compression ultrasound when used by experienced ermergency doctors searching for proximal deep vein thrombosis (DVT) in patients with no history of DVT. The gold standard is Doppler ultrasound of the lower limbs performed by a vascular exploration expert.

The main objective of this study is to describe the management of children with rheumatoid purpura from admission to the pediatric emergency room to follow-up by primary care physicians after hospital discharge.

The aim of this study is to describe the development of symptomatic and asymptomatic incisional hernias after emergency midline laparotomy over time in high-risk patients evaluated by consecutive MRI-scans.

ECHAS One will test the use of a smartphone app to assist patients to decide if it is necessary to call for emergency care with symptoms that could represent a heart attack or stroke.

Parents occupy a central place in the emergency care of suicidal adolescents and young adults. However, from 15 to 25yo, three different administrative situations exist in France: <16yo: admission to a child ED by a team trained to receive the youngest patients. 16-18yo: admission to an adult ED by team devoted to adult care, no exit without parents' authorization. >18yo: admission to an adult ED by team. Laying on qualitative observational protocol and a Delphi approach, this study will explore the perspective of adolescents and young adults following a suicidal attempt, the perspective of their parents, and the perspective of their healthcare professionals to build guidelines for parental involvement in care of suicidal youths.

A CVA occurs when there is a sudden interruption of blood supply to the brain. Fast identification of CVA is crucial in order to refer the patient to an appropriate medical center as well as to direct him/her to a suitable treatment upon arrival to the Medical Center, in order to minimize the permanent damage to the brain. In this study, we are evaluating a tool for detecting CVA based on EEG (electroencephalograph) data analysis using innovative algorithm. The system is comprised of four electrodes, reference electrode and earphones for auditory stimulation. In the study, 120 patients arriving to the emergency room with decreased consciousness, severe headaches or dizziness will be monitored for five-minute each, with EEG accompanied with auditory stimulation. The EEG analysis will be performed based on the synchronization of the front and back hemispheres. During CVA, specific hemisphere is damaged, therefore desynchronization is expected. The purpose of this study is to develop a tool for identifying CVA in patients who have no clear CVA related signs.

To determine if intravenous unfractionated heparin (with standard therapy) for treatment of anaphylaxis results in faster time to recovery.

Hyperglycaemic emergencies are associated with significant mortality (mortality in Diabetic Ketoacidosis 0.65 - 3.3% and in HyperOsmolar Non-Ketotic Coma 12 -17%). To reduce morbidity and mortality, prompt intervention and coma and close monitoring are essential. The study is designed to investigate whether a simple intervention with a long acting insulin can improve resolution of acidosis and hyperglycaemia, prevent recurrence of ketoacidosis and shorten hospital stay.

1) Establish a clinical data database for sepsis patients in the Emergency Department of the First Hospital of Jilin University. Describe the clinical data and prognosis of patients with simple systemic inflammatory response syndrome (common infection), pre sepsis, sepsis, and septic shock. 2) Explore the risk factors related to the progression of sepsis in patients in the early stages, as well as the risk factors related to the patient's prognosis. 3) Analyze the risk factors related to the prognosis of sepsis patients, and provide clinical basis for the treatment and long-term prognosis of sepsis patients. 4) Search for diagnostic biomarkers and prognostic serum biomarkers for patients with sepsis, sepsis, and septic shock.

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.