View clinical trials related to Pulmonary Embolism.
Filter by:The goal of this exploratory observational study is to assess the feasibility and real-world clinical impact of implementing Artificial Intelligence (AI) software for the detection of acute Pulmonary Embolism (PE) in patients who undergo Computed Tomography Pulmonary Angiogram (CTPA). The main questions that this study aims to answer are: [Question 1] What is the real-world impact of AI on the clinical outcomes and decision making by radiologists and clinicians in the management of acute PE? [Question 2] Is AI software for the detection of acute PE acceptable to use in clinical practice and do they have a favourable impact on clinical workload? [Question 3] Is it cost-effective to implement AI software for the detection of acute PE in clinical practice? Patients having a CTPA for the detection of acute PE will have their imaging analysed by AI software in combination with a human radiologist. Researchers will aim to compare the clinical and radiology specific outcomes with a retrospective cohort of patients who have had standard routine radiology reporting.
The goal of this clinical trial is to evaluate tailored duration of long-term anticoagulant treatment after a first venous thromboembolism based on individualized risk assessments of recurrent VTE and major bleeding risks. Participants will be asked to fill in a questionnaire and take a buccal swab, which are used for an individual estimation of the risks of recurrent VTE and bleeding. Based on these risks a treatment advise will be made, or randomised in a subgroup of patients.
to detect the incidence of CTEPH patients. Primary outcome To predict CETHP in symptomatic patients after pulmonary embolism episode. Secondary outcome: On Echocardiography revealed RV dysfunction which are peak TR Velocity , the RV/LV basal diameter ratio, flattening of the IVS, RV acceleration time and/or midsystolic notching, IVC diameter with decreased inspiratory collapse and RA area.
This is a registry of the patients that are admitted to CICU and treated by the Scientific Staff of the 2nd Department of Cardiology, due to an acute cardiovascular disease (acute coronary syndrome, acute heart failure, arrhythmia, pulmonary embolism, cardiac tamponade etc) in order to investigate the clinical characteristics of the patients, their outcome, identify the factors that could predict the in-hospital mortality and compare the results with the predicted by established risk scores. Furthermore, the study will investigate the one-year mortality and also the major adverse cardiac events (MACE - acute myocardial infarction, stroke, or cardiovascular death) will be measured.
Measure early out comes of surgical pulmonary embolectomy in patients with massive and sub massive pulmonary embolism.
Estimation of the incidence of pulmonary embolism in patients presenting to the Emergency Department with hemoptysis.
The investigators' aim in this study is to assess the sensitivity and accuracy of YEARS algorithm in the diagnosis of PE.
Evaluation of the Safety and Efficacy of the Symphony Thrombectomy System in the Treatment of Pulmonary Embolism
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.
This study is a prospective, multicenter, randomized controlled trial of the FlowTriever System plus anticoagulation compared to anticoagulation alone for intermediate-risk acute PE.