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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
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Evolution pattern estimated by computed tomography perfusion post-thrombectomy predicts outcome in acute ischemic
Xinyu Dai1, Chuming Yan2, Fan Yu1
1Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
Summary
Computed tomography perfusion and angiography can predict outcomes after endovascular thrombectomy for acute ischemic stroke. Combining imaging with clinical scores improves prediction accuracy for good outcomes.
Area of Science:
- Neuroimaging
- Stroke Medicine
- Interventional Neurology
Background:
- Computed tomography perfusion (CTP) and computed tomography angiography (CTA) are established for selecting acute ischemic stroke (AIS) patients for endovascular thrombectomy (EVT).
- Their utility in post-treatment evaluation for predicting clinical outcomes remains underexplored.
Purpose of the Study:
- To assess CTP and CTA abnormalities before and after EVT in AIS patients.
- To evaluate the potential of post-EVT CTP and CTA in predicting clinical outcomes.
Main Methods:
- Retrospective analysis of 83 AIS patients undergoing EVT with pre- and post-EVT CTP/CTA.
- Defined ischemic core and hypoperfusion volumes; tissue optimal reperfusion (TOR) was >90% hypoperfusion reduction.
- Assessed 90-day modified Rankin Scale (mRS) for clinical outcome.
Main Results:
- Patients with absent ischemic core or TOR showed better recanalization and outcomes.
- Baseline ischemic core volume, TOR, and immediate NIHSS score post-EVT were significant predictors of good 90-day mRS.
- A combined model of baseline core volume, TOR, and immediate NIHSS achieved high prediction performance (AUC=0.921).
Conclusions:
- Pre- and post-treatment CTP/CTA findings, alongside clinical scores, can enhance outcome prediction after EVT.
- Integrating advanced imaging with clinical assessments may optimize patient management and prognostication in AIS.
