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Background <p>Futile recanalisation (FR) defined as functional dependence despite successful reperfusion, is common after endovascular therapy for acute stroke. We aimed to evaluate whether the Vessel Distance Index (VDI), a novel imaging parameter, has the potential to predict the risk of FR.</p> Methods <p>Patients who underwent mechanical thrombectomy for acute anterior circulation large vessel occlusion between March 2022 and January 2025 were retrospectively reviewed. FR was defined as a modified Rankin Scale (mRS) score ≥ 3 at 90 days post-procedure. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of FR. The discriminatory ability of significant variables was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to compare FR risk models constructed from significant variables. Youden’s index was used to determine the optimal cut-off threshold of VDI. Additionally, the Jonckheere-Terpstra trend test was used to evaluate whether VDI levels showed a monotonic trend across cohorts stratified by occlusion site and infarct progression rate (IPR) associated with FR risk.</p> Results <p>A total of 26 patients, accounting for 41.3% of the cohort (<i>n</i> = 63), were identified with FR. Advanced age, higher NIHSS score, lower ASPECTS score, and higher VDI value were independently associated with FR (<i>p</i> &lt; 0.05). The ROC curve demonstrated that the combined model achieved the best predictive performance (AUC = 0.93, <i>p</i> &lt; 0.001), outperforming any single variable. VDI showed a significant upward trend with FR risk in high IPR, and MCA groups (<i>p</i> &lt; 0.05).</p> Conclusion <p>These findings suggest that VDI and traditional indicators can accurately predict long-term clinical outcomes before surgery and provide guidance for clinical practice.</p>

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Progress on using CTA-based vessel distance index to predict futile recanalization: a retrospective observational study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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  • Xiaoyue Hu,
  • Xin Liu,
  • Yiyun Fan,
  • Hao Lei,
  • Tianxin Zhang,
  • Hong Zhou,
  • Deicheng Zhao,
  • Zhaoxiang Zhang,
  • Ziling Huang,
  • Bang Luo,
  • Qing Ye,
  • Yulan Dong

摘要

Background

Futile recanalisation (FR) defined as functional dependence despite successful reperfusion, is common after endovascular therapy for acute stroke. We aimed to evaluate whether the Vessel Distance Index (VDI), a novel imaging parameter, has the potential to predict the risk of FR.

Methods

Patients who underwent mechanical thrombectomy for acute anterior circulation large vessel occlusion between March 2022 and January 2025 were retrospectively reviewed. FR was defined as a modified Rankin Scale (mRS) score ≥ 3 at 90 days post-procedure. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of FR. The discriminatory ability of significant variables was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to compare FR risk models constructed from significant variables. Youden’s index was used to determine the optimal cut-off threshold of VDI. Additionally, the Jonckheere-Terpstra trend test was used to evaluate whether VDI levels showed a monotonic trend across cohorts stratified by occlusion site and infarct progression rate (IPR) associated with FR risk.

Results

A total of 26 patients, accounting for 41.3% of the cohort (n = 63), were identified with FR. Advanced age, higher NIHSS score, lower ASPECTS score, and higher VDI value were independently associated with FR (p < 0.05). The ROC curve demonstrated that the combined model achieved the best predictive performance (AUC = 0.93, p < 0.001), outperforming any single variable. VDI showed a significant upward trend with FR risk in high IPR, and MCA groups (p < 0.05).

Conclusion

These findings suggest that VDI and traditional indicators can accurately predict long-term clinical outcomes before surgery and provide guidance for clinical practice.