View clinical trials related to Cardiac Arrest.
Filter by: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.
This is a single-center, observational study. Patients after successful cardiopulmonary resuscitation (CPR) will be transferred to the emergency intensive care unit for further standardized management. After successful return of spontaneous circulation (ROSC) for 72h and hemodynamics remained stable for 24h, the post-resuscitated patients underwent functional magnetic resonance imaging (fMRI) examination. During the examination, the supervising physician accompanied the patient and monitored the patient's vital signs using a magnetic resonance monitoring system (Siemens Healthcare Prism, Germany). Patients who are on ventilators are mechanically ventilated using a magnetic ventilator (HAMILTON-MRI, USA). In additional to conventional sequences, fMRI is performed for diffusion-prepared pseudo-continuous arterial spin labeling (DP-pCASL) and blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI). These MRI sequences allow quantitative assessment of the patients' cerebral microcirculation, blood-brain barrier, and cerebral oxygenation status. Patients will be followed up for neurologic prognosis according to the Modified Rankin Scale (mRS) at 6 months after disease onset.
This study is a multi-center, prospective, registry study. This research was supported by the National Key Research and Development Program. To establish a domestic multi-center, large-scale "brain-heart comorbidity" dynamic database platform including clinical, sample database, image and other multi-dimensional information requirements, through the construction of a multi-center intelligent scientific research integration platform based on artificial intelligence. Any of newly diagnosed cardiovascular related diseases were identified via ICD-10-CM codes: I21, I22, I24 (Ischaemic heart diseases) [i.e., ACS], I46 (cardiac arrest), I48 (Atrial fibrillation/flutter), I50 (Heart failure), I71 (Aortic disease), I60 (subarachnoid hemorrhage), I61 (intracerebral hemorrhage), I63 (Cerebral infarction), I65 (Occlusion and stenosis of precerebral arteries), I66 (Occlusion and stenosis of cerebral arteries), I67.1 (cerebral aneurysm), I67.5 (moyamoya diseases), Q28.2 (Arteriovenous malformation of cerebral vessels). The data is stored on the brain-heart comorbidity warehouse via a physical server at the institution's data centre or a virtual hosted appliance. The brain-heart comorbidity platform comprises of a series of these appliances connected into a multicenter network. This network can broadcast queries to each appliance. Results are subsequently collected and aggregated. Once the data is sent to the network, it is mapped to a standard and controlled set of clinical terminologies and undergoes a data quality assessment including 'data cleaning' that rejects records which do not meet the brain-heart comorbidity quality standards. The brain-heart comorbidity warehouse performs internal and extensive data quality assessment with every refresh based on conformance, completeness, and plausibility (http://10.100.101.65:30080/login).
Management of cardiac arrest according to published guidelines has remained largely unchanged for a decade. Thames Valley Air Ambulance provide Critical Care Paramedic and Physician teams who respond to cardiac arrests and offer treatments beyond the scope of ambulance service clinicians. Following a review of practice and appraisal of evidence the investigators developed an additional algorithm for cases of adult medical cardiac arrest with refractory shockable rhythms. This adds to but does not replace the Advanced Life Support algorithm and includes: - Delivering shocks with the LUCAS mechanical CPR device running - After 5 shocks have been delivered placing new pads in the Anterior Posterior (AP) position - Delivering shocks using the TVAA Tempus Pro defibrillator rather than the Ambulance Service defibrillator. This bundle was based on recommendations from ILCOR and the Resus Council (UK) Advanced Life Support manual and was launched in October 2021.
The high incidence rate, high Case fatality rate rate and high rate of neurological impairment of cardiac arrest pose a serious threat to the health of the whole population, and also bring a huge economic burden. In recent years, the "American Heart Association AHA Cardiopulmonary resuscitation and Cardiovascular Emergency Guide" has always emphasized the importance of "life chain" for the survival of patients with cardiac arrest. The hospital's survival chain emphasizes early warning recognition and activation of emergency response systems, immediate high-quality CPR, rapid defibrillation, advanced life support, and post arrest care. However, there is an urgent need for improvement and enhancement in all aspects of the chain of life for cardiac arrest. Millimeter wave radar can transmit radar signals that penetrate non-metallic substances such as clothing, detect the micro motion signals caused by human respiration and heartbeat, and then process the signals. By calculating the frequency or phase shift information in the radar echo, patient activity information can be obtained, achieving contactless and real-time detection of patient activity in the room. And it can achieve tracking of targets in scenarios where multiple people exist, while monitoring the physical signs of each target in real-time [7]; Our team has developed Cardiopulmonary resuscitation Quality Monitoring Index (CQI) and Cardiopulmonary resuscitation Ventilation Mode (CPRV) in the early stage, which are very helpful to monitor and improve the quality of Cardiopulmonary resuscitation; In recent years, the application of bedside echocardiography (PoCUS) in emergency has been significantly expanded. Although transthoracic echocardiography (TTE) can provide valuable diagnostic information for patients with cardiac arrest, it has important limitations in dynamic compression of Cardiopulmonary resuscitation. TEE can overcome many limitations of TTE, and the combination of the two can achieve visualization of resuscitation, Many signs of Cardiopulmonary resuscitation that had not been found before have been found. On the other hand, international guidelines recommend that the compression site of Cardiopulmonary resuscitation should be in the lower half of the sternum. However, research shows that there are great changes in the shape of the chest and the organizational structure directly below the compression site in normal people. The left ventricle is located in the lower quarter of the sternum, lower than the lower third of the sternum. When Cardiopulmonary resuscitation is carried out according to the current guidelines, only a small part of the ventricle is subjected to external compression, and for spinal deformity, obesity There is no corresponding research and recommendation for pregnant women and other special groups, and the extensive development of chest CT Iterative reconstruction provides the possibility of individualized evaluation. In addition, the COVID-19 in China has not yet been completely controlled. For patients suspected or confirmed to be infected with novel coronavirus, it is still challenging to carry out Cardiopulmonary resuscitation that may produce aerosols when wearing protective equipment. In summary, establishing a clinical decision-making system for the survival chain under the new situation and optimizing the survival chain process in the guidelines is of great significance for improving the survival rate and prognosis of patients with cardiac arrest, and is of great value for improving national health levels and reducing the economic burden on the government.
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards. eCART's algorithm is designed to analyze real-time EHR data, such as vitals and laboratory results, to identify which patients are at increased risk for clinical deterioration. The algorithm specifically predicts imminent death or the need for intensive care unit (ICU) transfer. Within the eCART interface, clinical teams are then directed toward standardized guidance to determine next steps in care for elevated-risk patients. The investigators hypothesize that implementing such a tool will be associated with a decrease in ventilator utilization, length of stay, and mortality for high-risk hospitalized adults.
WILLEM is a multi-center, prospective and retrospective cohort study. The study will assess the performance of a cloud-based and AI-powered ECG analysis platform, named Willem™, developed to detect arrhythmias and other abnormal cardiac patterns. The main questions it aims to answer are: 1. A new AI-powered ECG analysis platform can automatice the classification and prediction of cardiac arrhythmic episodes at a cardiologist level. 2. This AI-powered ECG analysis can delay or even avoid harmful therapies and severe cardiac adverse events such as sudden death. The prerequisites for inclusion of patients will be the availability of at least one ECG record in raw data, along with patient clinical data and evolution data after more than 1-year follow-up. Cardiac electrical signals from multiple medical devices will be collected by cardiology experts after obtaining the informed consent. Every cardiac electrical signal from every subject will be reviewed by a board-certified cardiologist to label the arrhythmias and patterns recorded in those tracings. In order to obtain tracings of relevant information, >95% of the subjects enrolled will have rhythm disorders or abnormal ECG's patterns at the time of enrollment.