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Introduction <p>Endovascular thrombectomy (EVT) patient selection depends on neuroimaging. However, interhospital transfer delays can lead to neuroimaging changes, whether and when repeat imaging is necessary are unclear. Herein, we develop a machine learning model (MLM) to predict vessel recanalization, ischemia progression, and imaging stability for EVT candidates who experience delayed interhospital transfer.</p> Methods <p>This retrospective study included EVT candidates with internal carotid or middle cerebral artery occlusion stroke transferred 1.5–6.0&#xa0;h after initial imaging. Clinical and radiographic data were collected. A gradient-boosted tree-based MLM (XGBoost) was trained and optimized on 66% of the cohort (randomly selected) using 10-fold cross-validation, and the MLM was independently validated on the remaining, untouched 33% of the study cohort. Model performance was assessed using areas under the receiver operating characteristics curve (AUC) for discrimination, F1 scores for precision/recall, and Brier scores for calibration.</p> Results <p>Among 317 patients, 69.4% had stable imaging, 14.5% showed ischemia progression (ASPECTS drop ≥ 2), and 16.1% had vessel recanalization. The MLM was developed and optimized in the training cohort (<i>n</i> = 212). NIH stroke scale improvement, onset-to-imaging time, intravenous thrombolysis, initial ASPECTS, and collateral score were important features. In the validation cohort (<i>n</i> = 105), the MLM achieved AUCs of 0.81 (95%CI 0.72–0.90) for imaging stability, 0.82 (95%CI 0.72–0.91) for ischemia progression, and 0.89 (95%CI 0.77-1.00) for vessel recanalization. F1 scores were 0.87 and 0.95 for stability and no recanalization, with Brier scores of 0.17 and 0.08, respectively.</p> Conclusion <p>Our MLM accurately predicts imaging changes among EVT candidates who experienced transfer delays.</p>

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Development of a machine learning model to predict changes in neuroimaging profiles among acute ischemic stroke patients following delayed transfer for endovascular thrombectomy

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Huanwen Chen,
  • Paige Skorseth,
  • Scott Rewinkel,
  • Daniel Kim,
  • Sonesh Amin,
  • Scott Shakal,
  • Ryan Priest,
  • Gary Nesbit,
  • Wayne Clark,
  • Marco Colasurdo

摘要

Introduction

Endovascular thrombectomy (EVT) patient selection depends on neuroimaging. However, interhospital transfer delays can lead to neuroimaging changes, whether and when repeat imaging is necessary are unclear. Herein, we develop a machine learning model (MLM) to predict vessel recanalization, ischemia progression, and imaging stability for EVT candidates who experience delayed interhospital transfer.

Methods

This retrospective study included EVT candidates with internal carotid or middle cerebral artery occlusion stroke transferred 1.5–6.0 h after initial imaging. Clinical and radiographic data were collected. A gradient-boosted tree-based MLM (XGBoost) was trained and optimized on 66% of the cohort (randomly selected) using 10-fold cross-validation, and the MLM was independently validated on the remaining, untouched 33% of the study cohort. Model performance was assessed using areas under the receiver operating characteristics curve (AUC) for discrimination, F1 scores for precision/recall, and Brier scores for calibration.

Results

Among 317 patients, 69.4% had stable imaging, 14.5% showed ischemia progression (ASPECTS drop ≥ 2), and 16.1% had vessel recanalization. The MLM was developed and optimized in the training cohort (n = 212). NIH stroke scale improvement, onset-to-imaging time, intravenous thrombolysis, initial ASPECTS, and collateral score were important features. In the validation cohort (n = 105), the MLM achieved AUCs of 0.81 (95%CI 0.72–0.90) for imaging stability, 0.82 (95%CI 0.72–0.91) for ischemia progression, and 0.89 (95%CI 0.77-1.00) for vessel recanalization. F1 scores were 0.87 and 0.95 for stability and no recanalization, with Brier scores of 0.17 and 0.08, respectively.

Conclusion

Our MLM accurately predicts imaging changes among EVT candidates who experienced transfer delays.