View clinical trials related to Cardiac Arrest.
Filter by:The investigators want to investigate the effect of rTMS on working memory measured by the N-back task. This is a single case experimental design, ABAB.
Severe trauma, head trauma, stroke and resuscitated cardiac arrest patients requiring endotracheal intubation and mechanical ventilation are at high risk of early-onset ventilator-associated pneumonia (EO-VAP). A short course of systemic antibiotic is recommended for prophylaxis. This study intends to assess the safety and efficacy of 2 alternative mechanical non-invasive airway clearance techniques in the prevention of EO-VAP in an open label randomized pilot trial of 20 subjects per study group i.e., 60 cases. The interventions will be in place for 7 days and the observational periods will be 14 days.
Monitoring risks of cardiovascular diseases in working population (18 - 65 years old) by monitoring their BMI, ankle-brachial index with pulse wave velocity, cholesterol and glycemia.
This study will assess the feasibility of performing pre-hospital resuscitative endovascular balloon occlusion of the aorta (REBOA) as an adjunct to conventional Advanced Life Support (ALS) in patients suffering from non-traumatic out of hospital cardiac arrest (OHCA). As well as providing valuable insights into the technical feasibility of performing this procedure as part of a resuscitation attempt, the study will also document the beneficial physiological effects of REBOA in this group of patients.
In order to monitor and improve cardiopulmonary resuscitation(CPR) quality, there is need for tools that provide real time feedback to responders. The use of invasive arterial pressure monitoring and end tidal carbon dioxide (ETCO2) as quality measures of CPR. Invasive pressure measurements are timeconsuming and cumbersome in resuscitation situations, and are very rarely practical. ETCO2 measurements require presence of a capnometer with an advanced airway. High quality chest compression will result inETCO2 between 2-2.5KPa. A rapid increase in ETCO2 on waveform capnography may enable ROSC to be detected while continuing chest compression and can be used as a tool to withhold the next dose of bolus adrenaline injection. Pulse oximetry, which noninvasively detects the blood flow of peripheral tissue, has achieved widespread clinical use. It was noticed that the pulse waveform frequency can reflect the rate and interruption time of chest compression(CC) during cardiopulmonary resuscitation(CPR). The perfusion index (PI) is obtained from pulse oximetry and is computed as the ratio of the pulsatile (alternating current) signal to the non-pulsatile (direct current) signal of infra-red light, expressed as a percentage;PI =ACIR/DCIR∗100% (i.e. AC = pulsatile component of the signal, DC = non-pulsatile component of the signal, IR = infrared light). PI shows the perfusion status of the tissue in the applied area for an instant and a certain time interval. The PI value ranges from 0.02% (very weak) to 20% (strong).Peripheral PI has been proposed for different clinical uses with some applications in critical patients. The purpose of this study is to evaluate the role of pulse-oximeter derived perfusion index for high quality CPR and as aprognostication tool of ROSC during in-hospital cardiac arrest in comparison to ETCO2 reading.
The goal of this observational study is to establish an intelligent early warning system for acute and critical complications of the respiratory system such as pulmonary embolism and respiratory failure. Based on the electronic case database of the biomedical big data research center and the clinical real-world vital signs big data collected by wearable devices, the hybrid model architecture with multi-channel gated circulation unit neural network and deep neural network as the core is adopted, Mining the time series trends of multiple vital signs and their linkage change characteristics, integrating the structural nursing observation, laboratory examination and other multimodal clinical information to establish a prediction model, so as to improve patient safety, and lay the foundation for the later establishment of a higher-level and more comprehensive artificial intelligence clinical nursing decision support system. Issues addressed in this study 1. The big data of vital signs of patients collected in real-time by wearable devices were used to explore the internal relationship between the change trend of vital signs and postoperative complications (mainly including infection complications, respiratory failure, pulmonary embolism, cardiac arrest). Supplemented with necessary nursing observation, laboratory examination and other information, and use machine learning technology to build a prediction model of postoperative complications. 2. Develop the prediction model into software to provide auxiliary decision support for clinical medical staff, and lay the foundation for the later establishment of a higher-level and more comprehensive AI clinical decision support system.
The goal of this study is to test the feasibility and acceptability of an informational website to reduce uncertainty, psychological distress, and caregiver burden among close family members of cardiac arrest patients. The investigators hypothesize that participants who receive access to the website will have lower rates of uncertainty, psychological distress, and caregiver burden at 3 months post-hospital discharge compared to participants who receive usual care.
Despite advances in post-resuscitation care of patients with cardiac arrest (CA), the majority of survivors who are treated after restoration of spontaneous circulation (ROSC) will have sequelae of hypoxic-ischemic brain injury ranging from mild cognitive impairment to a vegetative state. Early prognostication in comatose patients after ROSC remains challenging. Recent recommendations suggest carrying out clinical and paraclinical tests during the first 72 h after ROSC, to predict a poor neurological outcome with a specificity greater than 95% (no pupillary and corneal reflexes, bilaterally absent N20 somatosensory evoked potential wave, status myoclonus, highly malignant electroencephalography including suppressed background ± periodic discharges or burst-suppression, neuron-specific enolase (NSE) > 60 µg/L, a diffuse and extensive anoxic injury on brain CT/MRI), but with a low sensitivity due to frequent confounding factors. The heart rate variability (HRV) is a simple and non-invasive technique for assessing the autonomic nervous system function. In patients with a recent myocardial infarction, reduced HRV is associated with an increased risk for malignant arrhythmias or death. In neurology, reduced HRV is associated with a poor outcome in severe brain injury patients and allows to predict early neurological deterioration and recurrent ischemic stroke after acute ischemic stroke. A reduced HRV could be a sensitive, specific and early indicator of diffuse anoxic brain injury after CA. This multicenter prospective cohort study assesses the added value of early HRV (within 24h of ICU admission) for neuroprognostication after cardiac arrest.
In the course of prehospital respiratory and circulatory arrest, approximately 1000 persons are resuscitated by cardiopulmonary resuscitation in Upper Austria every year. Despite constant further development of methods, equipment and continuous training of the rescue and emergency medical teams working on site, the majority of patients who have to be resuscitated prehospital still die. However, even patients whose circulatory function can be restored during prehospital resuscitation (Return of Spontaneous Circulation, ROSC) require intensive medical care for days to weeks and often find it very difficult to return to a normal, independent life. The success of resuscitation measures depends on the quality of the resuscitation performed as well as on patient-specific factors. Evaluation scales such as the Cerebral Performance Category score (CPC) allow a posteriori assessment of resuscitation success. Nowadays, it is very difficult to estimate the outcome of resuscitation a priori. In many cases, it is not at all clear at the beginning of the treatment pathway whether the individual patient is expected to have an unfavorable prognosis in the context of respiratory arrest or whether a restitutio ad integrum is possible. Thus, the decision to continue or discontinue resuscitation can only be made on the basis of an individual physician's assessment. In addition to the primary concern of stopping resuscitation too early, there is also the risk that medical resources are used beyond the normal level after resuscitation without expecting a successful outcome. Estimating and categorizing the subsequent outcome is difficult and emotionally stressful for the treating team in the acute situation. Some factors that influence outcome are now known: As cerebral hypoperfusion increases, the probability of survival decreases sharply with each passing minute. In this context, potentially reversible causes have been identified in different works, allowing causal therapy to improve neurological outcome. In addition to the most important therapy bridging hypoperfusion, chest compression, with the aim of ensuring minimal perfusion of the brain, immediate defibrillation should be mentioned in particular, which now allows medical laypersons to use defibrillators as part of the Public Access Defibrillation Network. Despite all efforts, however, it is not yet possible to make reliable statements about the probable outcome of persons with respiratory and circulatory arrest with a high degree of certainty in a large number of cases at an early stage. Artificial intelligence refers to the ability of machines to perform cognitive tasks, such as recognizing objects in images and classifying them. For a long time, many processes were too complex to explore through sufficient computing power, storage capacity, and understanding. More recently, however, technological advances have brought machine learning (ML) and the constructs behind it, including those based on so-called neural networks (known since about 1950), back to the fore. Not only the development of theoretical models, but after extensive testing also devices applicable in daily routine operation are available. Modern machine learning methods are enabling a variety of new approaches to assessing operations, including modeling complex systems and finding relationships between models.
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?