View clinical trials related to Embolism.
Filter by:The initial aim is to build and validate artificial intelligence tools (machine learning and Natural Language Processing) to acquire and structure data from medical reports at the Centre Hospitalier Intercommunal de Toulon - la Seyne sur mer (CHITS). This project will build upon work previously done by the Department of Epidemiology, Biostatistics and Health Data (DEBDS) at the Centre Antoine Lacassagne (CAL) in Nice, focusing on breast and thyroid cancers. The idea is to validate the transferability of these tools to another establishment with different pathologies and practitioners, specifically the vascular medicine department at CHITS. Subsequently, the aim will be to identify clinically relevant phenotypes in patients with acute pulmonary embolism. Hierarchical clustering methods combined with unsupervised learning (machine learning) will be used to obtain groups of patients who are homogeneous at diagnosis. Evaluating their prognosis at 6 months (recurrence or chronic thromboembolic pulmonary hypertension), account the first 3 months of anticoagulant treatment, would provide an aid to medical decision-making. This research will include a retrospective and a prospective parts. The retrospective part will include patients who have been admitted to CHITS for acute pulmonary embolism since 2019. For the prospective part, it is planned to include patients with same characteristics over the years 2024 and 2025. More than 2,500 patients are expected to be included. This research will have no impact on current patient care. Data from consultations and various examinations carried out as part of care will be collected for six months post-diagnosis in order to meet the research objectives.
During hemodialysis (HD) the blood of the patient pass an extracorporeal circuit that contains a dialyzer for rinsing and a venous chamber (air trap) to prevent from air embolism through the return blood into the patient. However, air traps in clinical use have limited capacity to prevent from microemboli of air to enter the return bloodline and deposit as emboli in the body such as lungs, heart and brain. The Investigator developed the air trap Emboless that was patented. In vitro studies showed significantly better reduction of microemboli contaminations than air traps compared to that in clinical use. The present randomized clinical trial compares two different air traps used by the same patients in a cross-over design (as pairs) using the Emboless compared with the Fresenius 4008/5008 (F5008). Chronic HD patients are randomized to perform the first HD with either their standard air trap (F5008) in the venous bloodline tubing or using the Emboless bloodline and vice versa. Each patient was included to make two paired series. A safety committee evaluates if significantly worse outcome appears especially with the Emboless, to stop the study. During HD the microbubbles are counted by a GAMPT ultrasound device using two probes. One probe is set at the inlet side of the air trap and the second at the outlet side. The outlet side represents data of microbubbles in the blood that are entering into the patient. Comparative non-parametric paired analyses are performed between the air traps. Monitoring of the study is performed.
The purpose of this retrospective and prospective multicenter study is to evaluate the incidence of pulmonary hypertension (PH) within 6 months from ultrasound-assisted, Catheter-directed Thrombolysis for acute intermediate- high-risk Pulmonary Embolism
In this study, a new, non-invasive method for diagnosis of pulmonary embolism (PE) will be tested. In pre-clinical studies, the investigators have identified 151 putative biomarkers for pulmonary embolism in the exhaled breath, and several of these were also found in a first in-human clinical study what the investigators performed in 2019-2020 (DOI 10.1088/1752-7163/ad0aaa). These biomarkers need consolidation in a clinical setting using an updated collection device and proteomic analysis platform before development of a prototype and further test of this new diagnostic method.
The EMBOL-AF is a multicenter, international, observational study designed as a retrospective registry that will investigate the characteristics of systemic arterial embolic events after treatment of atrial fibrillation by catheter ablation. Due to the retrospective nature of the study, the registry is specially focused on cerebral embolism (stroke and TIA) because these are not only the most frequent and clinically relevant but also the most susceptible to underreporting. However, all embolism associated to AFAbl will be included. This study will gather all clinically relevant aspects and data of all cases of arterial embolism that have occurred over the last 5 years in the centers that will participate in the registry. Based on these reported cases, the incidence, management and outcomes of embolic events (particularly stroke and TIA) will be studied.
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.
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.
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.