The role of microsurgery for poor-grade aneurysmal subarachnoid hemorrhages in the endovascular era

Hanalıoğlu, Şahin; Şahin, Balkan; Sayyahmelli, Sima; Özaydın, Burak; Erginoğlu, Ufuk; Abakay, Abdurrahman; Başkaya, Mustafa K.

doi:10.1007/s00701-022-05112-1

Cited by 5 publications

(2 citation statements)

References 46 publications

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Section: Discussionmentioning

confidence: 99%

“…Similar to the WFNS grade, a higher mFS score indicates more extensive hemorrhage and are linked to unfavorable outcomes [15]. The severity of bleeding and its subsequent complications such as vasospasm and delayed cerebral ischemia contribute to the observed correlation between elevated mFS score and poor prognosis in high-grade aSAH [15,17,37]. Endovascular coiling, a minimally invasive strategy for the treatment of intracranial aneurysm, represents a promising technique for high-grade aSAH patients [38,39].…”

Section: Discussionmentioning

confidence: 99%

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Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage

Shu,

Yan,

et al. 2024

Aging

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Objective: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). Methods: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. Results: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. Conclusions: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

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