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Updated: Aug 1, 2025
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Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
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Predictors for large vessel recanalization before stroke thrombectomy: the HALT score
Marco Colasurdo1, Huanwen Chen1,2, Chad Schrier3
1Division of Interventional Neuroradiology, Department of Diagnostic Radiology, University of Maryland Medical Center, Baltimore, Maryland, USA.
Journal of Neurointerventional Surgery
|April 26, 2023
Summary
Predicting spontaneous large vessel recanalization (LVR) in acute ischemic stroke is crucial. The HALT score, using factors like IVT timing and occlusion site, effectively identifies patients unlikely to experience LVR before EVT.
Area of Science:
- Neurology
- Interventional Neuroradiology
- Stroke Medicine
Background:
- Large vessel recanalization (LVR) prior to endovascular therapy (EVT) for acute ischemic stroke is not well understood.
- Identifying predictors of LVR is vital for optimizing stroke triage and patient selection for bridging thrombolysis.
Purpose of the Study:
- To identify predictors of spontaneous LVR before EVT in acute large vessel ischemic stroke.
- To develop a predictive model for LVR to aid in clinical decision-making.
Main Methods:
- Retrospective cohort study of patients undergoing EVT for acute ischemic stroke (2018-2022).
- Data collected included demographics, clinical characteristics, intravenous thrombolysis (IVT) use, and LVR status.
- A multivariable prediction model was constructed to identify independent predictors of LVR.
Main Results:
- Of 640 patients, 57 (8.9%) experienced LVR before EVT.
- Independent predictors for LVR were identified: hyperlipidemia, atrial fibrillation, vascular occlusion site, and IVT timing (≥1.5 hours before angiography).
- The 8-point HALT score demonstrated strong predictive performance (AUC 0.85), with very low LVR rates (0.3%) in patients with low scores (0-2).
Conclusions:
- IVT timing, occlusion site, atrial fibrillation, and hyperlipidemia are key predictors of LVR.
- The HALT score is a valuable tool for predicting LVR before EVT, potentially improving patient selection and treatment strategies.
