View clinical trials related to Emergency Medical Services.
Filter by:This project is a randomized controlled clinical research design, The hypothesis P-I-C-O of the study is: For adult patients in the Taipei City and New Taipei City communities who have suffered sudden non-traumatic death and have been resuscitated by advanced paramedics, the intervention group that receives combined drug treatment (epinephrine, vasopressin, methylprednisolone) has a better rate of sustained recovery of spontaneous circulation (ROSC) (primary outcome) and long-term survival status (secondary outcomes) compared to the control group that receives single drug treatment (epinephrine).
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.
Rational: Out of hospital cardiac arrest is a devastating event with a high mortality. Survival rates have increased over the last years, with the availability of AED's and public BLS. Previous studies have shown that deranged physiology after return of spontaneous circulation (ROSC) is associated with a worse neurological outcome. Good quality post-arrest care is therefore of utmost importance. Objective: To determine how often prehospital crews (with their given skills set) encounter problems meeting optimal post-ROSC targets in patients suffering from OHCA, and to investigate if this can be predicted based on patient-, provider- or treatment factors. Study design: Prospective cohort study of all patients attended by the EMS services with an OHCA who regain ROSC and are transported to a single university hospital, in order to identify those patients with a ROSC after a non-traumatic OHCA who had deranged physiology and/or complications from OHCA EMS personnel was unable to prevent/deal with in the prehospital environment. Study population: Patients, >18 years, transported by the EMS services to the ED of the University Hospital Groningen (UMCG) with a ROSC after OHCA in a 1 year period Main study parameters/endpoints: Primary endpoint of our study is the percentage of OHCA patients with a prehospital ROSC who arrive in hospital with either a deranged physiology or with complications from OHCA EMS personnel was unable to deal with.