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Optimized System for Cerebral Perfusion Monitoring in the Rat Stroke Model of Intraluminal Middle Cerebral Artery Occlusion
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Prognostic value of multi-PLD ASL-based cerebral perfusion ASPECTS in acute ischemic stroke
Qingqing Li1,2, Chaojun Jiang3, Linqing Qian1
1Department of Radiology, Suzhou Wuzhong People's Hospital, Suzhou, Jiangsu, China.
Frontiers in Neurology
|October 24, 2024
Summary
The Alberta Stroke Program Early CT Score (ASPECTS) using multi-delay arterial spin labeling (ASL) effectively predicts outcomes in acute ischemic stroke (AIS) patients. Combining ASPECTS with NIHSS significantly improves outcome prediction accuracy.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Acute ischemic stroke (AIS) outcome prediction is crucial for treatment decisions.
- Early assessment of stroke severity and prognosis aids in patient management.
Purpose of the Study:
- To evaluate the Alberta Stroke Program Early CT Score (ASPECTS) using multi-post-labeling delay (multi-PLD) arterial spin labeling (ASL) for outcome assessment in AIS patients.
- To determine the prognostic value of ASL-derived ASPECTS compared to clinical factors.
Main Methods:
- ASPECTS were calculated using multi-PLD ASL in 55 AIS patients.
- Modified Rankin Scale (mRS) at 90 days was used as the primary outcome measure.
- Statistical analyses included t-tests, Mann-Whitney U, chi-squared, correlation, logistic regression, and ROC curve analysis.
Main Results:
- Higher cerebral blood flow (CBF)-ASPECTS and cerebral blood volume (CBV)-ASPECTS correlated with better 90-day outcomes.
- Baseline NIHSS, CBF-ASPECTS, and CBV-ASPECTS were independent prognostic indicators.
- The combination of NIHSS, CBF-ASPECTS, and CBV-ASPECTS achieved a high AUC of 96.3% for outcome prediction.
Conclusions:
- Multi-PLD ASL-based ASPECTS is a valuable tool for prognostic assessment in AIS.
- Combining baseline NIHSS with CBF-ASPECTS and CBV-ASPECTS significantly enhances predictive accuracy for clinical outcomes.
- CBV-ASPECTS alone demonstrates strong predictive efficacy, comparable to combined measures.

