Prediction of favorable outcomes of acute basilar artery occlusion using machine learning.

Journal: Journal of neurointerventional surgery
Published Date: Mar 13, 2026

Abstract

BACKGROUND: This study aims to develop an interpretable machine learning model using SHapley Additive exPlanations (SHAP) to predict favorable outcomes based on clinical, imaging, and angiographic data. METHODS: This study analyzed data from 184 patients with acute basilar artery occlusion (BAO) who underwent endovascular treatment (EVT) and completed a 90-day follow-up at Shanxi Provincial People's Hospital. A total of 68 medical variables were collected to develop predictive models using three machine learning algorithms: logistic regression (LR), support vector machine (SVM), and Light Gradient Boosting Machine (LightGBM). Model performance was comprehensively assessed using Accuracy, Recall, Precision, F1 score, and the area under the receiver operating characteristic curve (AUC-ROC), and the best-performing model's results were interpreted using SHAP. RESULTS: The SVM model demonstrated better performance, with an AUC of 0.899±0.059 (95% confidence interval (CI) 0.840 to 0.957), accuracy of 0.859±0.057, recall of 0.858±0.068, precision of 0.872±0.084, and an F1 score of 0.857±0.059. Recursive feature elimination with random forest (RF) and SHAP analysis revealed that the absence of ventilator use, absence of tracheotomy, lower National Institutes of Health Stroke Scale (NIHSS) scores at admission, and lower preoperative serum creatinine (SCR) levels were significant predictors of favorable 90-day outcomes. CONCLUSION: This study established a machine learning model to identify predictors of favorable outcomes in patients with acute BAO. Significant factors influencing prognosis included the use of mechanical ventilation, tracheotomy, NIHSS score, and preoperative SCR levels.

Authors

  • Yanqin Liu
    Big data Center for Nephropathy, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, China.
  • Pengyu Lu
    Department of Anesthesiology, Kaifeng Central Hospital, Kaifeng 475000, Henan, China.
  • Raynald -
    Beijing Tiantan Hospital, Department of Interventional Neuroradiology, Beijing, Beijing, China.
  • Yuanyue Lu
  • Wandi Liu
    Emergency Department, Huanhu Hospital, Jinnan, Tianjin, China.
  • Xiuping Li
    Neurology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Tingting Song
    Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yaxuan Sun
    Neurology, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Bin Han
    2 Department of Radiation Oncology, Stanford University, Stanford, CA, USA.

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