View clinical trials related to Heart Arrest.
Filter by:Current studies have shown that hospitalized ICU patients have a high risk of IHCA, with an incidence of about 0.6-7.8%. Early prediction of the occurrence of IHCA in severe patients can provide early intervention, prevent the deterioration of the disease, and reduce the incidence of IHCA. Therefore, researchers wanted to verify the efficacy of MEWS, NEWS, and CART scores in predicting IHCA in ICU inpatients, and to establish an early-warning scoring model that could effectively predict the risk of IHCA occurrence in ICU inpatients during hospitalization.
Differential diagnosis: ultrasound, blood gas analyse combined with examination of the patient and environment is used during Out Of Hospital Cardiac Arrest (OHCA) to screen possible underlying cause of cardiac arrest. Helicopter Emergency Medical Service (HEMS) in Helsinki has unraveled a protocol for performing differential diagnosis during OHCA. Our study aim is to test the feasibility of this protocol. Our study is a prospective medical record based study. The anesthesiologist operating in the HEMS unit fills a form after encounter of OHCA. Questioners focus on how long did execution of the protocol take, and the cause if the protocol was not completed.
Globally, it is estimated that 2.4 billion people suffer from caries of permanent teeth and 486 million children suffer from caries of primary teeth . The highest estimate of dental caries prevalence in Saudi Arabia was 95% with maximum DMFT of 7.34 among 3-7 years old children . Also, it was found that 78% of 6-9 years old children in Dammam had caries with an average of 3.66±3.13 DMFT. The objectives of this study are: 1. To assess the effectiveness of the Silver Diamine Fluoride (SDF) in arresting caries. 2. To assess the antibacterial effect of the Silver Diamine Fluoride (SDF). 3. To assess parents' and children's satisfaction and acceptance. The aims of our study are to assess the effectiveness of SDF in arresting caries in primary dentition among children visiting Imam Abdulrahman bin Faisal University in Dammam, Saudi Arabia and to assess parental acceptance and satisfaction. The expected outcome the results of this study will help in understanding the effectiveness SDF in arresting dental caries in this target population especially that it was recently approved for clinical use in Saudi Arabia.
Hypothesis: Implantation of on-scene ECPR by the HEMS teams in patients with sustained out-of-hospital cardiac arrest, results in the rapid return of circulation and, thus, improved survival and less neurological impairment, which is associated with lower health care costs.
The purpose of this study is to study the prevalence of post cardiac arrest syndrome (PCAS) among ICU patients, to analyze the effectiveness of intensive care, to assess the factors associated with death and the development of severe neurological deficits.
The primary aim of this trial is to assess the efficacy of resuscitative endovascular balloon occlusion of the aorta (REBOA) as an adjunct treatment to advanced cardiovascular life support (ACLS) in patients with out-of-hospital cardiac arrest. Out of hospital cardiac arrest (OHCA) carries a high mortality rate. The treatment of out-of-hospital cardiac arrest is ACLS as stated in the guidelines from the Norwegian Resuscitation Council and the European Resuscitation Guidelines. Recently, REBOA has been proposed as an adjunct treatment in management of non-traumatic cardiac arrest patients, because thoracic aortic occlusion provides a redistribution of the cardiac output to organs proximal to the occlusion. Preclinical studies demonstrate that REBOA during CPR provide both increased coronary artery blood flow and perfusion pressure and increased rates of return of spontaneous circulation (ROSC). This is the first prospective trial in the world to assess the efficacy of REBOA in non-traumatic cardiac arrest. The intervention is shown feasible in the pre-hospital setting. If this trial provides a signal of benefit in patients, this study could initiate further clinical research which could change current resuscitation practice world-wide.
Trial to assess the ability of a wearable to detect defibrillation from an implantable ICD
Evaluation of clinical, logistical, and technical factors (e.g. implementation of pre-arrival instructions, implementation of the DAE RespondER, mechanical chest compressor, REBOA, and E-CPR) that impact on the outcomes of out-of-hospital cardiac arrest patients.
The overall incidence of cardiorespiratory arrest in Europe is estimated at 350,000 to 700,000 cases per year. Survival rate is estimated at 10.7% for all rhythm disorders combined. Several examples of AI application in the medical field exist. Ting et al have developed a computer tool capable of diagnosing the presence of diabetic retinopathy with excellent power. In resuscitation, Celi et al proposed a tool capable of predicting the need for crystalloid vascular filling during a systemic inflammatory state. In Nature in 2018, Komorowski demonstrated the efficacy of AI in the hemodynamic management of sepsis. In a study of the renal response to fluid challenge, Zhang et al. demonstrate the effectiveness of the learning machine. Objectives: Determination of an algorithm capable of predicting the mortality of patients admitted to intensive care units (ICU) for ACR from hospitalization reports (CRH). Also use of the algorithm to predict the risk of recurrence of the arrest, the duration of mechanical ventilation, the appearance of sepsis, the development of organ failure, prediction of the CPC (Cerebral Performance Category), time to obtain catecholamine withdrawal, the appearance of acute renal failure with or without the need for extra-renal purification (EER) and duration under EER, the average length of stay. This project is part of a larger, nationwide project with greater power, and includes all the data generated during hospitalization in intensive care. Method: an estimated total number of patients included in this study to be between 300 and 500. The population will come from the intensive care units of Nice, Antibes, Cannes, Grasse. Inclusion will be retrospective, on CRH, CR of CT imaging (cerebral and thoraco-abdomino-pelvic), MRI, EEG, and daily follow-up words, from 2014 to the end of 2020. After anonymisation, application of semantisation using natural language processing (NLP) methods. The data to be extracted are entered in a document written by intensive care physicians. These data will then be stored in a database. In order to meet the main objective, we will develop a computer algorithm capable of predicting mortality in the study population. This algorithm, based on a large database, can be designed using machine learning or even deep learning techniques depending on the amount of data to be processed.
The study aims to increase proportions of bystander defibrillation during out-of-hospital cardiac arrest (hereof referred to as cardiac arrest) in residential areas with a high density of cardiac arrests. The intervention consists of Automated External Defibrillators (AEDs) and residents' involvement in resuscitation through training and enrollment as citizen responders.