Clinical-Diffusion Mismatch Is Associated with Early Neurological Improvement after Late-Window Endovascular Treatment

Endovascular therapy in acute ischaemic stroke with large infarction with matched or mismatched clinical-radiological severities: a post-hoc analysis of the ANGEL-ASPECT trial

Background

Endovascular therapy (EVT) was demonstrated effective in acute large vessel occlusion (LVO) with large infarction. Revealing subgroups of patients who would or would not benefit from EVT will further inform patient selection for EVT.

Methods

This post-hoc analysis of the ANGEL-ASPECT trial, a randomised controlled trial of 456 adult patients with acute anterior-circulation LVO and large infarction, defined by ASPECTS 3-5 or infarct core volume 70-100 mL, enrolled from 46 centres across China, between October 2, 2020 and May 18, 2022. Patients were randomly assigned (1:1) to receiving EVT and medical management or medical management alone. One patient withdrew consent, 455 patients were included in this post-hoc analysis and categorised into 4 subgroups by lower or higher NIHSS (< or ≥16) and smaller or larger infarct core (< or ≥70 mL). Those with lower NIHSS & smaller core, and higher NIHSS & larger core were considered clinical-radiological matched subgroups; otherwise clinical-radiological mismatched subgroups. Primary outcome was 90-day modified Rankin Scale (mRS). ANGEL-ASPECT is registered with ClinicalTrials.gov, NCT04551664.

Findings

Overall, 139 (30.5%) patients had lower NIHSS & smaller core, 106 (23.3%) higher NIHSS & larger core, 130 (28.6%) higher NIHSS & smaller core, and 80 (17.6%) lower NIHSS & larger core. There was significant ordinal shift in the 90-day mRS toward a better outcome with EVT in clinical-radiological matched subgroups: lower NIHSS & smaller core (generalised OR, 1.76; 95% CI, 1.18-2.62; p = 0.01) and higher NIHSS & larger core (1.64; 1.06-2.54; 0.01); but not in the two clinical-radiological mismatched subgroups.

Interpretation

Our findings suggested that in patients with anterior-circulation LVO and large infarction, EVT was associated with improved 90-day functional outcomes in those with matched clinical and radiological severities, but not in those with mismatched clinical and radiological severities. Simultaneous consideration of stroke severity and infarct core volume may inform patient selection for EVT.

Funding

Unrestricted grants from industry [Covidien Healthcare International Trading (Shanghai), Johnson & Johnson MedTech, Genesis MedTech (Shanghai), and Shanghai HeartCare Medical Technology].

Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images

Purpose: To develop a preliminary deep learning model that uses diffusion-weighted imaging (DWI) images to classify the severity of neurological impairment caused by ischemic stroke. Materials and Methods: This retrospective study included 851 ischemic stroke patients (711 patients in the training set and 140 patients in the test set). The patients’ NIHSS scores, which reflect the severity of neurological impairment, were reviewed upon admission and on Day 7 of hospitalization and were classified into two stages (stage 1 for NIHSS < 5 and stage 2 for NIHSS ≥ 5). A 3D-CNN was trained to predict the stage of NIHSS based on different preprocessed DWI images. The performance in predicting the severity of anterior and posterior circulation stroke was also investigated. The AUC, specificity, and sensitivity were calculated to evaluate the performance of the model. Results: Our proposed model obtained better performance in predicting the NIHSS stage on Day 7 of hospitalization than that at admission (best AUC 0.895 vs. 0.846). Model D trained with DWI images (normalized with z-score and resized to 256 × 256 × 64 voxels) achieved the best AUC of 0.846 in predicting the NIHSS stage at admission. Model E rained with DWI images (normalized with maximum−minimum and resized to 128 × 128 × 32 voxels) achieved the best AUC of 0.895 in predicting the NIHSS stage on Day 7 of hospitalization. Our model also showed promising performance in predicting the NIHSS stage on Day 7 of hospitalization for anterior and posterior circulation stroke, with the best AUCs of 0.905 and 0.903, respectively. Conclusions: Our proposed 3D-CNN model can effectively predict the neurological severity of IS using DWI images and performs better in predicting the NIHSS stage on Day 7 of hospitalization. The model also obtained promising performance in subgroup analysis, which can potentially help clinical decision making.