What is the clinical evidence from this study?

Study design: RCT. Population: large core infarction (n=146). Intervention: Endovascular Treatment (EVT) vs. Medical therapy alone. Primary outcome: Futile recanalisation defined as mRS score 4-6 at 90 days post-EVT (HR null, 95% CI null, p=null).

February 2, 2026Stroke and Vascular Neurology0 citationsOpen Access

Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction

YGYawen Gan SSShuang Song DZDingwen Zhang

Key Result

The support vector machine model achieved an AUC of 0.891 (95% CI 0.837 to 0.945) for predicting futile recanalisation after EVT in patients with large core infarction.

Key Points

The aim is to determine effective machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarctions.
Patients from the EVT trial were categorized into two groups based on the modified Rankin Scale.
Feature selection was performed using the least absolute shrinkage and selection operator regression.
Four machine learning algorithms were applied: logistic regression, support vector machine, decision tree, and random forest.
Model performance was assessed using receiver operating characteristic curves and calibration plots.
Out of 146 patients, 74 faced futile recanalisation.
Eight key predictors of futile recanalisation were identified, ranked by SHAP analysis.
The support vector machine model had the highest area under the curve of 0.891.
The SVM model showed satisfactory calibration and clinical utility.

Study Design

Type

RCT (n=146)

Blinding

null

Randomization

null

Multicenter

Yes

Structured PICO

Does a support vector machine model accurately predict 90-day futile recanalisation in patients with large core infarction following successful endovascular treatment?

Population

146 patients with acute anterior circulation large vessel occlusion and large infarct core who achieved successful reperfusion (expanded Thrombolysis in Cerebral Infarction Score ≥2b) following endovascular treatment. Multicenter (46 comprehensive stroke centres in China).

Intervention

Support vector machine (SVM) machine learning model using 8 preinterventional predictors (sex, age, NIHSS, glucose, systolic blood pressure, neutrophil-to-lymphocyte ratio, fibrinogen, and occlusion site) to predict futile recanalisation.

Comparator

Logistic regression, decision tree, and random forest machine learning models.

Outcome

Futile recanalisation, defined as successful reperfusion (eTICI≥2b) but without independent ambulation (modified Rankin Scale score 4-6) at 90 days post-EVT.hard clinical

A support vector machine model incorporating eight routine clinical variables accurately predicts the risk of futile recanalisation at 90 days in patients with large core infarction following endovascular treatment.

Main Result

Effect estimate: HR null (95% CI null)

p-value: p=null

Limitations

Limited sample size of 146 patients
Exclusion of patients with incomplete baseline data may introduce selection bias
Study population primarily sourced from China, which may limit generalizability.

Abstract

Background Predicting futile recanalisation following endovascular treatment (EVT) in patients with large core infarctions is crucial for guiding clinical decisions, optimising perioperative management and improving healthcare resource allocation. This study aimed to compare four machine learning (ML) algorithms and identify the most effective model for preinterventional prediction of futile recanalisation. Methods Patients achieving successful reperfusion (expanded Thrombolysis in Cerebral Infarction Score≥2b) from the EVT in Acute Anterior Circulation Large Vessel Occlusive Patients With a Large Infarct Core trial were stratified into two groups: no-futile recanalisation (90-day modified Rankin Scale (mRS) 0–3) and futile recanalisation (mRS 4–6). The least absolute shrinkage and selection operator regression method was used for feature selection, and four ML algorithms, including logistic regression, support vector machine (SVM), decision tree and random forest, were applied. Model performance was evaluated using receiver operating characteristic curves, calibration plots and decision curve analysis. Feature importance was ranked using SHapley Additive exPlanation (SHAP) values. Results Among 146 patients, 74 experienced futile recanalisation. Eight predictors were identified and ranked by SHAP analysis from highest to lowest importance: sex, age, National Institutes of Health Stroke Scale, glucose, systolic blood pressure, neutrophil-to-lymphocyte ratio, fibrinogen and occlusion site. Among the four models, the SVM model achieved the highest area under the curve of 0.891 (95% CI 0.837 to 0.945), along with good calibration (Hosmer-Lemeshow test, p=0.103) and clinical utility. Conclusion The SVM model emerges as the optimal predictive tool for futile recanalisation following EVT in patients with large core infarction. Nevertheless, external validation is required to confirm its performance before clinical application. Trial registration number NCT04551664 .

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Cite This Study

Gan et al. (Thu,) conducted a rct in large core infarction (n=146). Endovascular Treatment (EVT) vs. Medical therapy alone was evaluated on Futile recanalisation defined as mRS score 4-6 at 90 days post-EVT (HR null, 95% CI null, p=null). The support vector machine model achieved an AUC of 0.891 (95% CI 0.837 to 0.945) for predicting futile recanalisation after EVT in patients with large core infarction.

synapsesocial.com/papers/6980fc73c1c9540dea80e49b https://doi.org/https://doi.org/10.1136/svn-2025-004258

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