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Zheng L, Nie X, Wang M, Liu X, Duan W, Zhang Z, Liu J, Wei Y, Wen M, Yang Z, Leung TW, Ma G, Huo X, Pan Y, Nguyen TN, Leng X, Miao Z, Liu L. 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. EClinicalMedicine 2024; 72:102595. [PMID: 38638180 PMCID: PMC11024914 DOI: 10.1016/j.eclinm.2024.102595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/23/2024] [Accepted: 03/26/2024] [Indexed: 04/20/2024] Open
Abstract
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].
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Affiliation(s)
- Lina Zheng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Ximing Nie
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Mengxing Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Xin Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Wanying Duan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Jingyi Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Yufei Wei
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
| | - Miao Wen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhonghua Yang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Thomas W. Leung
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Gaoting Ma
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
- Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaochuan Huo
- Cerebrovascular Disease Department, Neurological Disease Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Yuesong Pan
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
- Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Thanh N. Nguyen
- Department of Neurology, Radiology Boston Medical Centre, MA, USA
| | - Xinyi Leng
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zhongrong Miao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
- Interventional Neuroradiology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liping Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Centre for Neurological Diseases, Beijing, China
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Zeng Y, Long C, Zhao W, Liu J. Predicting the Severity of Neurological Impairment Caused by Ischemic Stroke Using Deep Learning Based on Diffusion-Weighted Images. J Clin Med 2022; 11:jcm11144008. [PMID: 35887776 PMCID: PMC9325315 DOI: 10.3390/jcm11144008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/23/2022] [Accepted: 07/05/2022] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
- Ying Zeng
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Department of Radiology, Xiangtan Central Hospital, Xiangtan 411199, China
| | - Chen Long
- Department of Stroke Unit, Xiangtan Central Hospital, Xiangtan 411199, China;
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Clinical Research Center for Medical Imaging, Changsha 410011, China
- Correspondence: (W.Z.); (J.L.)
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China;
- Clinical Research Center for Medical Imaging, Changsha 410011, China
- Department of Radiology Quality Control Center, Changsha 410011, China
- Correspondence: (W.Z.); (J.L.)
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