Predictive Modeling of Symptomatic Intracranial Hemorrhage Following Endovascular Thrombectomy: Insights From the Nationwide TREAT-AIS Registry.
Journal
Journal of stroke
Journal Volume
27
Journal Issue
1
Start Page
85
End Page
94
ISSN
2287-6391
Date Issued
2025-01
Author(s)
Chen, Jia-Hung
Su, I-Chang
Lu, Yueh-Hsun
Hsieh, Yi-Chen
Lin, Chun-Jen
Chen, Yu-Wei
Lin, Kuan-Hung
Sung, Pi-Shan
Tang, Chih-Wei
Chu, Hai-Jui
Fu, Chuan-Hsiu
Chou, Chao-Liang
Wei, Cheng-Yu
Yan, Shang-Yih
Chen, Po-Lin
Yeh, Hsu-Ling
Sung, Sheng-Feng
Lin, Ching-Huang
Lee, Meng
Lee, I-Hui
Chan, Lung
Lien, Li-Ming
Chiou, Hung-Yi
Lee, Jiunn-Tay
DOI
10.5853/jos.2024.04119
Abstract
Background and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.
Subjects
Endovascular thrombectomy
Prediction
Symptomatic intracranial hemorrhage
Publisher
Korean Stroke Society
Type
journal article