Active clinical trials for "Ischemic Stroke"

Patients with ischemic stroke in the posterior circulation continue to have high rates of mortality and disability, even with aggressive treatment. We wanted to evaluate preoperative imaging to screen patients with a good prognosis from mechanical embolization. We assess the degree of ischemia by defining the pons-midbrain-medulla index (PMMI) and correlate the preoperative PMMI with the clinical prognosis of postoperative patients to verify the validity of PMMI in predicting the clinical prognosis of patients with embolization.

This study is a multicenter, randomized, double-blind, placebo parallel control study, aim to evaluate the efficacy and safety of human urinary kallidinogenase in the treatment of acute ischemic stroke with type 2 diabetes.

The objective of the study is to investigate the effectiveness and safety of rhTNK-tPA in acute ischemic stroke patients within 4.5 hours of symptom onset in a real-world clinical setting.

The objectives of the study are to examine the performance and safety characteristics of the Millipede System when used for revascularization of patients with acute ischemic stroke due to Large Vessel Occlusions (LVOs) and to record associated clinical outcomes.

Ischemic post-conditioning is a neuroprotective strategy that has been proven to attenuate reperfusion injury in animal models of stroke. The investigators have conducted a 3 + 3 dose-escalation trial to demonstrate the safety and tolerability of ischemic post-conditioning incrementally for a longer duration of up to 5 min × 4 cycles in stroke patients undergoing mechanical thrombectomy. The purpose of this study is to further determine the efficacy and safety of ischemic post-conditioning in patients with acute ischemic stroke who are treated with mechanical thrombectomy.

Atrial Fibrillation (AF) is an abnormal heart rhythm. Because AF is often asymptomatic, it often remains undiagnosed in the early stages. Anticoagulant therapy greatly reduces the risks of stroke in patients diagnosed with AF. However, diagnosis of AF requires long-term ambulatory monitoring procedures that are burdensome and/or expensive. Smart devices (such as Apple or Fitbit) use light sensors (called "photoplethysmography" or PPG) and motion sensors (called "accelerometers") to continuously record biometric data, including heart rhythm. Smart devices are already widely adopted. This study seeks to validate an investigational machine-learning software (also called "algorithms") for the long-term monitoring and detection of abnormal cardiac rhythms using biometric data collected from consumer smart devices. The research team aims to enroll 500 subjects who are being followed after a stroke event of uncertain cause at the Emory Stroke Center. Subjects will undergo standard long-term cardiac monitoring (ECG), using FDA-approved wearable devices fitted with skin electrodes or implantable continuous recorders, and backed by FDA-approved software for abnormal rhythm detection. Patients will wear a study-provided consumer wrist device at home, for the 30 days of ECG monitoring, 23 hours a day. At the end of the 30 days, the device data will be uploaded to a secure cloud server and will be analyzed offline using proprietary software (called "algorithms") and artificial intelligence strategies. Detection of AF events using the investigational algorithms will be compared to the results from the standard monitoring to assess their reliability. Attention will be paid to recorded motion artifacts that can affect the quality and reliability of recorded signals. The ultimate aim is to establish that smart devices can potentially be used for monitoring purposes when used with specialized algorithms. Smart devices could offer an affordable alternative to standard-of-care cardiac monitoring.

First prospective, single-arm, multicentre study to evaluate the safety and efficacy of the overall stroke thrombectomy system: INEDIT, INDEEP and INTERCEPT in patients with acute ischemic stroke.

Chronic intracranial arterial occlusion is associated with a "bidirectional stroke risk," with a significantly increased risk of both ischemic stroke and cerebral hemorrhage. Currently, Western CTAP products, in combination with clinical expertise, offer some predictive value for assessing the risk of ischemic events by evaluating compensatory pathways and overall perfusion in chronic intracranial arterial occlusion. However, there is limited support for assessing the risk of hemorrhagic events. Our proposed project aims to address a significant scientific challenge: the precise assessment of long-term stroke risk in asymptomatic patients with chronic intracranial arterial occlusion using a machine learning-based approach. The rapidly advancing field of machine learning provides a rich set of solutions for tackling this problem. In this project, we intend to develop a deep learning-based segmentation model for key brain regions using multimodal CT scans. Subsequently, we will automate the extraction of radiomic features and CT perfusion parameters, followed by the application of machine learning techniques to construct a stroke risk prediction model tailored for patients with chronic intracranial arterial occlusion.

The purpose of this study is to investigate the safety and feasibility of intra-arterial albumin infusion for patients with acute ischemic stroke after successful thrombectomy and to further explore the optimal dose of albumin through the implementation of a 3 + 3 dose-escalation design.

The purpose of this study is to determine the safety and feasibility of using inhaled nitric oxide (iNO) in patients undergoing intra-arterial mechanical thrombectomy (blood clot extraction or IAMT) for treatment of acute ischemic (non-bleeding) stroke (AIS).