Safety and efficacy of adjunct tirofiban treatment following mechanical thrombectomy for acute ischemic stroke patients with large vessel occlusion (LVO) resulting in successful reperfusion

An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis

Background and objective

Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS.

Methods

This study retrospectively collected data from 180 SICAS stroke patients treated at the hospital between 2020.01 and 2022.01. Relevant imaging and clinical data were collected, and follow-up was conducted. The dataset was divided into a training set and a validation set in a ratio of 7:3. We employed the least absolute shrinkage and selection operator (LASSO) regression to perform a selection on the baseline data, laboratory tests, and neuroimaging data generated by HR-VWI scans collected from the training set. Finally, five machine learning techniques, including logistic regression model (LR), support vector machine (SVM), Gaussian naive Bayes (GNB), Complement naive Bayes (CNB), and k-nearest neighbors algorithm (kNN), were employed to develop a predictive model for stroke recurrence. Shapley Additive Explanation (SHAP) was used to provide visualization and interpretation for each patient. The model's effectiveness was evaluated using average accuracy, sensitivity, specificity, precision, f1 score, PR curve, calibration curve, and decision curve analysis.

Results

LASSO analysis revealed that "history of hypertension," "homocysteine level," "NWI value," "stenosis rate," "intracranial hemorrhage," "positive remodeling," and "enhancement grade" were independent risk factors for stroke recurrence in SICAS patients. In 10-fold cross-validation, the area under the curve (AUC) ranged from 0.813 to 0.912 in ROC curve analysis. The area under the precision-recall curve (AUPRC) ranged from 0.655 to 0.833, with the Gaussian Naive Bayes (GNB) model exhibiting the best ability to predict stroke recurrence in SICAS. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of stroke recurrence in SICAS.

Conclusion

A precise machine learning-based prediction model for stroke recurrence in SICAS has been established to assist clinical practitioners in making clinical decisions and implementing personalized treatment measures.

Meta-analysis of the efficacy and safety of tirofiban in patients with acute ischaemic stroke undergoing mechanical thrombectomy

INTRODUCTION

Mechanical thrombectomy is now widely used in acute ischaemic stroke, but its adjunctive antiplatelet aggregation regimen is controversial. This study aimed to investigate the safety and efficacy of tirofiban in patients with acute ischaemic stroke (AIS) who underwent mechanical thrombectomy.

METHODS

We systematically searched Pubmed, Embase, Cochrane Library, and Web of science. Randomized controlled studies and cohort studies comparing the tirofiban group and non-tirofiban group (control group) in patients with AIS who underwent mechanical thrombectomy. The primary safety outcomes were symptomatic intracranial hemorrhage (sICH), 3-month mortality, and re-occlusion rate. The primary efficacy outcomes were good functional outcome (mRS 0-2), excellent functional outcome (mRS 0-1), and successful recanalization (mTICI≥2b).

RESULTS

We included 22 studies with a total of 6062 patients. For safety outcomes, the tirofiban group had a non-significantly higher rate of sICH (OR = 0.90, 95 % CI = 0.73-1.10, P = 0.29) and a significantly lower rate of re-occlusion (OR = 0.40, 95 % CI = 0.19-0.82, P = 0.01) and 3-month mortality (OR = 0.71, 95 % CI = 0.61-0.82, P < 0.00001) compared to the control group. In terms of efficacy outcomes, significant improvement in good functional outcomes (mRS 0-2) (OR = 1.24, 95 % CI = 1.11-1.39, P = 0.0002) and recanalization rate (OR = 1.38, 95% CI = 1.17-1.62, P = 0.0001) compared to tirofiban, but not significant improvement in excellent functional outcomes(OR = 1.14, 95 % CI = 0.93-1.39, P = 0.21). In addition, compared with cardiogenic stroke, the large atherosclerotic stroke had a higher rate of good functional outcome (OR = 1.58, 95 % CI = 1.18-2.11, P = 0.002) and a lower rate of 3-month mortality (OR = 0.58, 95 % CI = 0.39-0.85, P = 0.005). Subgroup analysis by route of administration showed a significant improvement in good functional outcome in the intravenous group (OR = 1.27, 95 % CI = 1.08-1.50, P = 0.004), while no significant difference was found between the arterial and arteriovenous groups.

CONCLUSION

Treatment with tirofiban in patients with AIS with mechanical thrombectomy is effective in improving functional prognosis, arterial recanalization rates, and reducing 3-month mortality and re-occlusion rates, particularly in patients with large atherosclerotic stroke, without increasing the rate of symptomatic intracranial hemorrhage. Intravenous administration of tirofiban significantly improves the clinical prognosis compared to arterial administration. Tirofiban is effective and safe in patients with AIS.