View clinical trials related to Cardiac Arrest.
Filter by:In this manikin-based simulation study the impact of tele-support during a simulated pediatric out-of-hospital cardiac arrest scenario on emergency medical technicians' guideline adherence, on gaze behavior as well as on performance of resuscitation management and cognitive load will be analyzed.
This study aims to compare the effect of conventional cardiopulmonary resuscitation (CPR) training and CPR training with added virtual reality (VR) on trainee self-efficacy.
To provide detailed information on long-term outcomes in relation to potential neuroprotection and improvements in recovery for different targets of sedation, temperature, and pressure management in post out of hospital cardiac arrest survivors at 6 and 12 months. In addition, the impact of caring for a post OHCA survivor will be explored.
The Discover In-Hospital Cardiac Arrest (IHCA) study is a multicenter, prospective observational study aimed at better understanding variations in practice for the post-in-hospital cardiac arrest patient.
The purpose of the research (pilot study) is to determine the impact of the use of the autotransfusion device on hemodynamic parameters during resuscitation. 24 people will be included in the pilot study (12 people will be included in the intervention group - with the usage of "autotransfusion socks" during resuscitation and 12 people in the control group - without "autotransfusion socks"). Investigators will compare the hemodynamic parameters and also neurological outcome between both groups.
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.
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.
The goal of this observational study is to evaluate whether thigh muscle mass and muscle wasting are associated with mortality in patients who visit the emergency department. The main questions it aims to answer are: - Is thigh muscle mass associated with mortality in patient who visit the emergency department? - Does muscle wasting exist during staying in the emergency department? - Is muscle wasting associated with mortality in patient who visit the emergency department? Participants will be evaluated for serial thigh muscle mass using point-of-care ultrasound at the emergency department.