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Updated: Jan 11, 2026
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
Published on: January 18, 2018
Systematic evaluation of predictive models for futile recanalization before thrombectomy in patients with acute
Cheng Chen1, Lei Liu2, Xiaoling Liu3
1Department of Pain, Suining Central Hospital, Suining, Sichuan, China.
Objective:
To systematically review existing predictive models for futile recanalization after mechanical thrombectomy in patients with acute ischemic stroke, in order to provide a basis for treatment decision-making.
Methods:
Relevant studies on predictive models of futile recanalization after mechanical thrombectomy for acute ischemic stroke were searched in PubMed, Web of Science, Embase, The Cochrane Library, CNKI, Wanfang, and VIP databases from inception to May 5, 2024. Reference lists were also manually searched as supplements. Two researchers independently performed the literature search, screening, and data extraction, and conducted risk of bias and quality assessments. Because most included studies did not provide 95% confidence intervals or standard errors of AUC values, a formal quantitative meta-analysis of model performance was not feasible. Instead, we conducted a stratified descriptive synthesis of AUC values according to modeling approach (traditional regression vs. machine learning/deep learning).
Results:
Thirteen studies were included, encompassing 23 predictive models for futile recanalization. Variables used in the models mainly involved baseline clinical and imaging features. The most frequently included predictors were age, NIHSS score, baseline mRS score, and baseline Alberta Stroke Program Early CT Score (ASPECTS). The AUC of the models ranged from 0.650 to 0.981, with 11 models reporting AUC values ≥0.8, indicating high predictive performance.
Conclusion:
Predictive models for futile recanalization after mechanical thrombectomy in acute ischemic stroke are still under development. While many models exhibit good discrimination, they commonly face a high risk of bias. Future research should emphasize external validation and optimization of existing models to improve their performance, reduce bias, and promote clinical implementation.
Systematic Review Registration:
The systematic review was registered in PROSPERO under the ID CRD42022382797. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022382797.
