Predictors of futile recanalization following endovascular therapy for acute anterior circulation large vessel occlusion stroke: a retrospective cohort study

 

I don't think you identified cause and effect properly, measuring Rankin scores has nothing directly to do with reperfusion! My god, the blithering stupidity out there is astounding!

 What followup research did you do ensure reperfusion will work completely every time? Oh NO, YOU INCOMPETENTLY DID NOTHING, right? Predicting failure is totally fucking useless! 

But this futile recanalization probably is because you did NOTHING to stop the 5 causes of the neuronal cascade of death in the first week and thus letting die hundreds of millions to billions of neurons! The recanalization worked, you just don't recognize this neuronal cascade of death occurring! 

You are COMPLETELY FUCKING INCOMPETENT FOR NOT KNOWING THAT! Your mentors are just as incompetent!

You really don't  know what the fuck you are doing in stroke, so get the hell out!

Predictors of futile recanalization following endovascular therapy for acute anterior circulation large vessel occlusion stroke: a retrospective cohort study

• Y

  Yangchun Wen ^2†

• 

  Xi Liu ^2†

• X

  Xiujing Liao ^1†

• G

  Guanghong Zhong ¹

• H

  Hongli Gu ¹

• 

  Jia Zhang ¹

• 

  Ying Xie ^1,3*

• 1. Heyuan People’s Hospital, Heyuan, Guangdong, China

• 2. Department of Neurology, Heyuan Hospital of Traditional Chinese Medicine, Heyuan, China

Abstract

Background: 

Endovascular therapy (EVT) is effective for acute anterior circulation large vessel occlusion (LVO), yet futile recanalization remains common. Identifying its predictors is essential to optimize patient selection.

Methods: 

This retrospective cohort study included 311 patients with anterior circulation LVO who underwent successful EVT. Patients were divided into an effective recanalization group (90-day modified Rankin Scale (mRS) score ≤ 2, n = 150) and a futile recanalization group (mRS > 2, n = 161). Clinical, imaging, and procedural variables were compared. Multivariate logistic regression was used to identify independent predictors, and a predictive model was evaluated using receiver operating characteristic (ROC) curve analysis.

Results: 

Baseline comparisons between the two groups showed significant differences in age, sex, atrial fibrillation, stress hyperglycemia ratio (SHR), C-reactive protein, admission National Institutes of Health Stroke Scale (NIHSS) score, diffusion-weighted imaging (DWI) infarct volume, DWI-Alberta Stroke Program Early CT Score (DWI-ASPECTS) score, number of procedure attempts, and procedure time. Univariate logistic regression and multivariate logistic regression identified admission NIHSS score (OR = 1.381, 95% CI: 1.113–1.713, p = 0.003), DWI infarct volume (OR = 1.057, 95% CI: 1.025–1.090, p < 0.001), DWI-ASPECT score (OR = 69.240, 95% CI: 9.819–488.254, p < 0.001), procedure time (OR = 1.011, 95% CI: 1.005–1.018, p < 0.001), and SHR (OR = 3.447, 95% CI: 1.302–9.129, p = 0.013) as independent predictors of futile recanalization. A combined model incorporating these five predictors achieved an area under the curve (AUC) of 0.815 (sensitivity: 0.789, specificity: 0.787, Youden index: 0.576).

Conclusion: 

Admission NIHSS score, SHR, DWI infarct volume, DWI-ASPECT score, and procedure time are independent predictors of futile recanalization after EVT for anterior circulation LVO stroke. Higher NIHSS score, elevated SHR, larger infarct volume, lower DWI-ASPECT score, and prolonged procedure time increase the risk of poor functional outcome despite successful reperfusion.

Clinical software for unsupervised automated net water uptake analysis predicts futile recanalization in acute ischemic stroke

 You are that BLITHERINGLY STUPID you don't know that predictions are useless? Survivors would like EXACT RECOVERY PROTOCOLS! GET THERE!

Clinical software for unsupervised automated net water uptake analysis predicts futile recanalization in acute ischemic stroke

• 

  Abdallah Aburub 1† *

• O

  Oussama Dob 1†

• M

  Mariana Gurschi 1

• Y

  Yashar Aghazadeh 1

• J

  Jumana Jaber 1

• Z

  Zaid Al-Tamimi 2

• Z

  Zaid Samhan 3

• L

  Lars Timmermann 4

• 

• 

• 

• A

  André Kemmling 1,9‡

• 1. Department of Diagnostic and Interventional Neuroradiology, Philipps University of Marburg, Marburg, Germany

• 2. Clinic of Diagnostic and Interventional Radiology, Philipps University of Marburg, Marburg, Germany

Abstract

Objectives: 

This study aimed to determine whether automated net water uptake (NWU) measured on admission non-contrast computed tomography (CT) independently predicts futile recanalization (FR) in anterior-circulation large vessel occlusion (LVO) stroke treated with endovascular thrombectomy (EVT).

Methods: 

This was a retrospective single-center cohort study that included consecutive patients presenting at a tertiary care stroke center between January 2023 and April 2025. Patients were included if they presented with an anterior circulation LVO and had successful recanalization (mTICI 2b–3). An automated platform (VEOcore/MRAY) provided the Alberta Stroke Program Early CT Score (ASPECTS), perfusion metrics, and NWU. Associations with FR were analyzed using logistic regression. Discrimination was evaluated using receiver operating characteristic (ROC) analysis and Youden’s J statistic to identify an optimal NWU threshold, reporting the area under the curve (AUC), sensitivity, specificity, and predictive values.

Results: 

Among 91 patients, 62 (68.1%) achieved recanalization with a 90-day modified Rankin Scale (mRS) score of 0–4, and 29 (31.9%) met the criteria for FR (mRS score 5–6). Those with FR were older (81.6 ± 8.3 vs. 76.9 ± 10.7 years; p = 0.044) and had more severe strokes at presentation (median National Institutes of Health Stroke Scale [NIHSS] score of 16 [13–20] vs. 9.5 [6–15]; p < 0.001). NWU in the core was higher in FR (21.9% [7.0–29.4]) than in non-FR cases (3.0% [0.5–7.3]; p < 0.001) and correlated with 90-day mRS (Pearson r = 0.602; 95% CI 0.452–0.719; p < 0.001). In multivariable models, NWU remained independently associated with FR (odds ratio [OR] 1.15; 95% CI 1.06–1.24; p < 0.001).

Conclusion: 

Automated NWU on admission CT is an independent, strongly discriminative predictor of FR and 90-day disability. A tiered strategy using >11.5% as a risk flag and >17.5% as a high-specificity rule-in threshold may enhance early triage, prognostication, and clinical trial stratification. Prospective multicenter validation is warranted.

Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke

 Did you even objectively identify futile recanalization? I don't think you identified cause and effect properly, measuring Rankin scores has nothing directly to do with reperfusion! My god, the blithering stupidity out there is astounding!

 What followup research did you do ensure reperfusion will work completely every time? Oh NO, YOU INCOMPETENTLY DID NOTHING, right? Predicting failure is totally fucking useless! 

But it probably is because you did NOTHING to stop the 5 causes of the neuronal cascade of death in the first week and thus letting die hundreds of millions to billions of neurons!

You really don't  know what the fuck you are doing in stroke, so get the hell out!

Multimodal CT radiomics-clinical ensemble machine learning model effectively predicts futile recanalization after endovascular treatment of acute ischemic stroke

• 

  Zhenxiong Wang 1†

• Y

  Yidong Gao 1†

• P

  Pan Xu 2†

• D

  Di Wu 3

• W

  Wuying Li 2

• H

  Huameng Huang 2

• W

  Weihua Deng 2

• H

  Honggang Xu 1

• X

  Xinhua Wei 1

• 

  Xing Li 2*

• 1. Department of Radiology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China

• 2. Department of Neurology, Guangzhou First People's Hospital, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China

Abstract

Backgrounds: 

Futile recanalization (FR) poses a significant challenge in endovascular treatment and there is a lack of reliable predictive models for assessing treatment outcomes in stroke. The aim of this study is to develop a robust CT radiomics-clinical ensemble model that predicts FR in patients with acute ischemic stroke (AIS) following endovascular treatment (EVT) utilizing machine learning techniques.

Methods: 

This study enrolled 101 patients diagnosed with AIS who underwent successful EVT. A total of 946 radiomics features were, respectively, extracted from non-contrast CT (NCCT), contrast-enhanced CT (CECT), and various CT perfusion maps (CBF, CBV, MTT, and TTP) using PyRadiomics prior to the endovascular intervention. Demographic characteristics, along with baseline clinical, laboratory, and angiographic variables, were incorporated as clinical features in the model analysis. Feature engineering was performed using SelectKBest. Five traditional machine learning algorithms were employed for modeling. The dataset was randomly split into a training cohort (n = 71, 70%) and an internal validation cohort (n = 30, 30%). Receiver operating characteristic (ROC) curves were utilized to evaluate the performance of each model.

Results: 

Among the 101 patients, FR occurred in 66 individuals (65%), as determined by the modified Rankin Scale (mRS) at 90 days. The ensemble model integrating clinical data, NCCT, and CBV achieved the highest performance, with an area under the curve (AUC) of 0.918 using the CatBoost algorithm.

Conclusion: 

The multimodal CT radiomics-clinical ensemble machine learning model demonstrated excellent predictive capability for identifying FR in AIS patients with large vessel occlusion prior to EVT.