Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT06451887 |
Other study ID # |
KY2024058 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
April 1, 2024 |
Est. completion date |
June 2027 |
Study information
Verified date |
June 2024 |
Source |
Zhejiang Provincial People's Hospital |
Contact |
Sheng Zhang |
Phone |
18758188313 |
Email |
zhangsheng[@]hmc.edu.cn |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational [Patient Registry]
|
Clinical Trial Summary
To design and validate a predictive model for malignant brain edema after endovascular
thrombectomy.
Description:
Stroke is a significant global cause of death and disability. Endovascular thrombectomy (EVT)
is currently the best treatment for acute large vessel occlusion stroke (ALVOS), as it can
greatly reduce mortality and improve patient outcomes. However, only half of patients who
undergo EVT achieve functional independence, and malignant brain edema (MBE), a severe
complication, can occur after the procedure, leading to a poor prognosis. Previous studies
have confirmed the effectiveness of early decompressive hemicraniectomy in reducing morbidity
and mortality in patients with malignant brain edema. Therefore, identifying high-risk
patients for MBE can help clinicians make appropriate triage and early intervention
decisions, potentially saving patients' lives.
Predictive factors for post-ischemic stroke brain edema have been widely discussed, and
reliable early predictive indicators have been identified, such as age, early consciousness
disorders, baseline National Institutes of Health Stroke Scale (NIHSS), atrial fibrillation,
hypertension, baseline blood glucose, and the level of reperfusion after EVT. Radiological
factors, such as the Alberta Stroke Program Early CT Score (ASPECTS), collateral circulation
rating of arteries, venous outflow status, CT perfusion core infarct volume, perfusion-based
collateral status, and clot burden, are closely associated with the occurrence of MBE
post-EVT. However, due to individual differences and multiple factors affecting MBE, a single
factor cannot effectively predict MBE. Establishing a clinical risk prediction model can
effectively identify high-risk populations for MBE at an early stage.