Predictive Modeling of Symptomatic Intracranial Hemorrhage Following Endovascular Thrombectomy: Insights From the Nationwide TREAT-AIS Registry.

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Tác giả: Lung Chan, Chih-Hao Chen, Jia-Hung Chen, Po-Lin Chen, Yu-Wei Chen, Hung-Yi Chiou, Chao-Liang Chou, Hai-Jui Chu, Chuan-Hsiu Fu, Yi-Chen Hsieh, Jiann-Shing Jeng, I-Hui Lee, Jiunn-Tay Lee, Meng Lee, Li-Ming Lien, Ching-Huang Lin, Chun-Jen Lin, Kuan-Hung Lin, Hon-Man Liu, Yueh-Hsun Lu, I-Chang Su, Pi-Shan Sung, Sheng-Feng Sung, Chih-Wei Tang, Sung-Chun Tang, Cheng-Yu Wei, Shang-Yih Yan, Hsu-Ling Yeh

Ngôn ngữ: eng

Ký hiệu phân loại: 331.7 Labor by industry and occupation

Thông tin xuất bản: Korea (South) : Journal of stroke , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 193327

 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. CONCLUSION: s 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.
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