Observational studyBMC neurology2026
Progress on using CTA-based vessel distance index to predict futile recanalization: a retrospective observational study.
Observational study in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
12 authors.
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Abstract
backgroundFutile 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.
methodsPatients 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.
resultsA 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).
conclusionThese findings suggest that VDI and traditional indicators can accurately predict long-term clinical outcomes before surgery and provide guidance for clinical practice.
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