View clinical trials related to Cardiac Arrest.
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).
In this retrospective study, the investigators seek to investigate the incidence of pneumothorax following possible risk factors, and elucidate its association with outcomes.
Oxidative stress is one of the main mechanisms causing harm in severe infection with septic shock, ischemia-reperfusion injury in resuscitated cardiac arrest and ischemic and hemorrhagic stroke. Melatonin is a potent scavenger of the mediators of oxidative stress, oxygen and nitrogen-reactive species, which directly injure cell structures like walls and DNA and thus cause organ dysfunction. In a previous study we have observed that high-dose oral bedtime melatonin (OBM) is associated with improved organ function in severe Covid-19 patients
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.
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.