View clinical trials related to Cardiac Arrest.
Filter by:In this retrospective study, the investigators seek to investigate the incidence of pneumothorax following possible risk factors, and elucidate its association with outcomes.
The goal of this clinical trial is to evaluate the incidence of bradycardia during laparoscopic cholecystectomy. The main question[s] it aims to answer are: - Does bradycardia really occurs during pneumoperitoneum/laparoscopic surgery? - If the patient get Glycopyrrolate, Does it really prevent pneumoperitoneum/laparoscopic surgery induced bradycardia?
The investigators aimed to investigate the effect of delayed hospitalization on the basis of the call time on the clinical outcomes of patients with OHCA patients using a nationwide OHCA registry.
Determination the success rate of CPR on adults in Emergency room and predicting the factors that makes CPR is successful.
Airway management in out-of-hospital cardiac arrest is still debated. Several options exist: bag-valve-mask ventilation, supraglottic devices and endotracheal intubation. Intermediate and advanced airway management strategies could be useful devices to increase chest compression fraction. A previous study shows that early insertion of an i-gel device significantly increases chest compression fraction and enhances respiratory parameters. However, the compressions were found to be shallower in the experimental group using the i-gel device. Although, the shallower compressions found in the supraglottic airway device group did not appear to be linked to their provision in an over-the-head position, it is reasonable to assume that the addition of a feedback device to the use of an i-gel® device could fix this issue. The feedback devices seem to be able to provide a benefit, and allow deeper compressions / more often in the depth target. There is a mismatch between perceived and actual cardiopulmonary resuscitation performance supporting the need for such a feedback device's study.
Sudden cardiac arrest is a major public health problem worldwide and it is one of the leading causes of death in industrialized countries. Emergency Medical Services (EMS) dispatchers play an important role to recognize cardiac arrest and give help to the lay first responder via telephone CPR (T-CPR) which improves survival rates. The current technology allows the live video connection between the scene and the dispatcher which provides the opportunity for video-assisted CPR (V-CPR) via the bystander smartphone. Effectiveness of V-CPR has only been investigated to a limited extent. Comparing effectiveness of V-CPR (effectiveness of chest compression, time parameters eg. time to first chest compression) to T-CPR and non-instructed CPR can be useful to implement V-CPR technology.
Cardiac arrest (CA) is a worldwide health problem and is associated with high mortality and morbidity rates. After CA, most patients are exposed to cerebral injury due to anoxic perfusion, resulting in severe neurological deficits. Return of spontaneous circulation (ROSC) after KA causes acute cerebral edema with increased intracranial pressure (ICP) due to ischemia-reperfusion and delayed hyperemia, and deterioration of cerebral perfusion. This reduces the quality of life of most patients after cardiac arrest.
Pediatric cardiac arrest occurs most in the prehospital setting. Most of them are due to respiratory failure (e.g., trauma, drowning, respiratory distress), where hypoxia leads to cardiac arrest. Generally, emergency medical services (EMS) first use basic airway management techniques i.e., the use of a bag-valve-mask (BVM) device, to restore oxygenation in pediatric OHCA victims. However, these devices present many drawbacks and limitations. Intermediate airway management, i.e., the use of SGA devices, especially the i-gel® has several advantages. It has been shown to enhance both circulatory and ventilatory parameters. There is increasing evidence that IAM devices can safely be used in children. In two pediatric studies of OHCA, American paramedics had significantly higher success rates with SGA devices than with TI. A neonatal animal model showed that the use of SGA was feasible and non-inferior to TI in this population. However, data regarding the effect of IAM with an i-gel® versus the use of a BVM on ventilation parameters during pediatric OHCA is missing. The hypothesis underlying this study is that, in case of pediatric OHCA, early insertion of an i-gel® device without prior BVM ventilation should improve ventilation parameters in comparison with the standard approach consisting in BVM ventilations.
The objective of this observational and retrospective study is to determine the predictive factors of in-hospital mortality following an out-of-hospital cardiopulmonary arrest (CPA) in the population under 18 years old. Data are collected from telephone calls and medical regulation records processed by the health call center of 2 french departments between January 1, 2019, and March 15, 2022. The medical records of the included patients will also be reread in order to obtain the patient's status at 30 days after the CPA. Detailed description: The literature reports numerous works evaluating the epidemiological characteristics of pediatric out-of-hospital cardiorespiratory arrest. An improvement in survival has been reported in the case of resuscitation guided by the operator in medical regulation before the arrival of the emergency services. Indeed, the regulation phase at the 15 center in France is of fundamental importance. Recent evolutions, notably with the creation of specific call-taking professions, show the importance attached to improving practices. The population concerned is characterized by children under 18 years of age, victims of an extra-hospital cardiorespiratory arrest. It is a retrospective study over three years and three months, multicentric, from the emergency service (SAMU) 57 and 69. The primary endpoint was the all-cause mortality at thirty days of the admission. The case report form (CRF) will collect the main aspects of telephone management at the 15 centers, out-of-hospital management by the emergency teams, and the personal characteristics of the emergency physicians and out-of-hospital responders (gender, age, family situation, etc.). The medical management in the emergency department and the first stages of in-hospital management will also be analyzed. The patients included who are still alive will receive a notification of non-objection by mail.
Intrahospital cardiovascular arrest is one of the most common causes of death in hospitalized patients. In contrast to extramural cases of cardiovascular arrest, hospitalized patients often have severe medical conditions that can affect the outcome of resuscitation. Nevertheless, survival rates from resuscitation are better in hospitals than outside, because there is often a rapid start of resuscitation measures and predefined resuscitation standards. Regular CPR training and the availability of defibrillators in all bedside units can also positively influence outcome. Despite these many efforts, survival rates, especially of patients with good neurological outcome, remained stable at low levels even within hospitals in recent years and did not improve. Most outcome parameters are nowadays well known. (e.g., initial rhythm, age, early defibrillation, etc.) Nevertheless, we still do not know today how relevant the corresponding factors actually are, especially in relation to each other. One approach to this might be machine learning methods such as "random forest", which might be able to create a predictive model. However, this has not been attempted to date. The hypothesis of this work is to find out if it is possible to accurately predict the probability of surviving an in-hospital resuscitation using the machine learning method "random forest" and if particularly relevant outcome parameters can be identified. Design: retrospective data analysis of all data sets recorded in the resuscitation register of Kepler University Hospital. Measures and Procedure: Review of the registry for missing data as well as false alarms of the CPR team and, if necessary, exclusion of these data sets; evaluation of the data sets using the machine learning method random forest.