Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models.

Background: Symptomatic intracranial hemorrhage (sICH) is a severe complication of reperfusion therapy for ischemic stroke. Multiple models have been developed to predict sICH or intracranial hemorrhage (ICH) after reperfusion therapy. We provide an overview of published models and validate their ability to predict sICH in patients treated with endovascular treatment in daily clinical practice.

Timing of symptomatic intracranial hemorrhage after endovascular stroke treatment.

Introduction: Little is known about the timing of occurrence of symptomatic intracranial hemorrhage (sICH) after endovascular therapy (EVT) for acute ischemic stroke. A better understanding could optimize in-hospital surveillance time points and duration. The aim of this study was to delineate the probability of sICH over time and to identify factors associated with its timing.

Type of intracranial hemorrhage after endovascular stroke treatment: association with functional outcome.

Background: Intracranial hemorrhage (ICH) is a frequent complication after endovascular stroke treatment.

Objective: To assess the association of the occurrence and type of ICH after endovascular treatment (EVT) with functional outcome.

Methods: We analyzed data from the MR CLEAN-NO IV and MR CLEAN-MED trials.

Safety and efficacy of periprocedural antithrombotics in patients with successful reperfusion after endovascular stroke treatment.

Objectives: We aimed to evaluate whether the overall harmful effect of periprocedural treatment with aspirin or heparin during endovascular stroke treatment is different in patients with a successful reperfusion after the procedure.

Materials And Methods: We performed a post-hoc analysis of the MR CLEAN-MED trial, including adult patients with a large vessel occlusion in the anterior circulation eligible for endovascular treatment (EVT). In this trial, patients were randomized for periprocedural intravenous treatment with aspirin or no aspirin (1:1 ratio), and for moderate-dose unfractionated heparin, low-dose unfractionated heparin or no unfractionated heparin (1:1:1 ratio).

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks.

Background And Purpose: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as generative adversarial networks do not. The aim of this study was to develop and evaluate a generative adversarial network to segment infarct and hemorrhagic stroke lesions on follow-up NCCT scans.