Stroke; a journal of cerebral circulation
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Randomized Controlled Trial
Ischemic Core and Hypoperfusion Volumes Correlate With Infarct Size 24 Hours After Randomization in DEFUSE 3.
Background and Purpose- Accurate prediction of the subsequent infarct volume early after stroke onset helps determine appropriate interventions and prognosis. In the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke), we evaluated the accuracy of baseline ischemic core and hypoperfusion volumes for predicting infarct volume 24 hours after randomization to endovascular thrombectomy versus medical management. We also assessed if the union of baseline ischemic core and the volume of persistent hypoperfusion at 24 hours after randomization predicts infarct volume. ⋯ Conclusions- The union of the irreversibly injured ischemic core and persistently hypoperfused tissue volumes, as identified by computed tomography perfusion or magnetic resonance diffusion weighted imaging/perfusion, predicted infarct volume at 24 hours after randomization in DEFUSE 3 patients. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT02586415.
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Multicenter Study
Impact of Balloon Guide Catheter Use on Clinical and Angiographic Outcomes in the STRATIS Stroke Thrombectomy Registry.
Background and Purpose- Mechanical thrombectomy has been shown to improve clinical outcomes in patients with acute ischemic stroke. However, the impact of balloon guide catheter (BGC) use is not well established. Methods- STRATIS (Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke) was a prospective, multicenter study of patients with large vessel occlusion treated with the Solitaire stent retriever as first-line therapy. ⋯ Conclusions- BGC use was an independent predictor of FPE, modified FPE, and functional independence, suggesting that its routine use may improve the rates of early revascularization success and good clinical outcomes. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT02239640.
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Background and Purpose- Triaging of referrals to transient ischemic attack (TIA) clinics is aided by risk stratification. Deep learning-based natural language processing, a type of machine learning, may be able to assist with the prediction of cerebrovascular cause of TIA-like presentations from free-text information. Methods- Consecutive TIA clinic notes were retrieved from existing databases. ⋯ The greatest AUC was achieved when the convolutional neural network was provided with the history of presenting complaint and magnetic resonance imaging report (88.3±3.6). Conclusions- Deep learning-based natural language processing, in particular convolutional neural networks, based on medical free-text, may prove effective in prediction of the cause of TIA-like presentations. Future research investigating the role of the application of deep learning-based natural language processing to the automated triaging of clinic referrals in TIA, and potentially other specialty areas, is indicated.