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<p>Endovascular treatment (EVT) for vertebrobasilar artery occlusion (VBAO) with atrial fibrillation presents complex clinical challenges. This comprehensive multicenter study of 525 patients across 15 Chinese provinces investigated nuanced predictors beyond conventional metrics. While 45.1% achieved favorable outcomes at 90 days, our advanced machine learning approach unveiled subtle interaction effects among clinical variables not captured by traditional statistical methods. The predictive model distinguished high-risk subgroups by integrating multiple parameters, demonstrating superior prognostic precision compared to standard NIHSS-based assessments. Novel findings include nonlinear relationships between dyslipidemia, stroke severity, and functional recovery. The developed predictive algorithm (AUC 0.719 internally, 0.684 externally) offers a more sophisticated risk stratification tool, potentially guiding personalized treatment strategies in high-complexity VBAO patients with atrial fibrillation.</p>

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AI prediction model for endovascular treatment of vertebrobasilar occlusion with atrial fibrillation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月2日

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  • Zhi-Xin Huang,
  • Andrea M. Alexandre,
  • Alessandro Pedicelli,
  • Xuying He,
  • Quanlong Hong,
  • Yongkun Li,
  • Ping Chen,
  • Qiankun Cai,
  • Aldobrando Broccolini,
  • Luca Scarcia,
  • Serena Abruzzese,
  • Carlo Cirelli,
  • Mauro Bergui,
  • Andrea Romi,
  • Erwah Kalsoum,
  • Giulia Frauenfelder,
  • Grzegorz Meder,
  • Simona Scalise,
  • Maria Porzia Ganimede,
  • Luigi Bellini,
  • Bruno Del Sette,
  • Francesco Arba,
  • Susanna Sammali,
  • Andrea Salcuni,
  • Sergio Lucio Vinci,
  • Giacomo Cester,
  • Luisa Roveri,
  • Xianjun Huang,
  • Wen Sun

摘要

Endovascular treatment (EVT) for vertebrobasilar artery occlusion (VBAO) with atrial fibrillation presents complex clinical challenges. This comprehensive multicenter study of 525 patients across 15 Chinese provinces investigated nuanced predictors beyond conventional metrics. While 45.1% achieved favorable outcomes at 90 days, our advanced machine learning approach unveiled subtle interaction effects among clinical variables not captured by traditional statistical methods. The predictive model distinguished high-risk subgroups by integrating multiple parameters, demonstrating superior prognostic precision compared to standard NIHSS-based assessments. Novel findings include nonlinear relationships between dyslipidemia, stroke severity, and functional recovery. The developed predictive algorithm (AUC 0.719 internally, 0.684 externally) offers a more sophisticated risk stratification tool, potentially guiding personalized treatment strategies in high-complexity VBAO patients with atrial fibrillation.