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Purpose <p>Stroke risk correlates with the Biffl grading system in blunt cerebrovascular injury (BCVI). Although anti-thrombotic therapy is the mainstay of stroke prevention, no point-of-care clinical decision-support tool exists to guide timing for therapy. We sought to develop an interactive online calculator that incorporates patient-specific demographic and injury characteristics to estimate stroke risk and risk reduction with anti-thrombotic (AT) administration.</p> Methods <p>Data from BCVI patients (<i>n</i> = 1,197) at a Level I Trauma Center were retrospectively collected. Six machine learning methods were employed to predict stroke risk with and without AT therapy. Class imbalance was addressed using downsampling and/or class weighting. Model performance was assessed using 10-fold cross-validation. The model was implemented as an R-based Shiny online application.</p> Results <p>Stroke rate among the population was 4%, and the strongest predictors for stroke were the greatest Biffl grade of carotid (aOR [95%CI] = 2.02 [1.62–2.53]) and vertebral injuries (1.44 [1.18–1.77]). The least absolute shrinkage and selection operator (LASSO) model outperformed all others, achieving 66% [33%–100%] sensitivity and 74% [62%–82%] specificity for stroke prediction, with an area under the receiver operating characteristic curve of 0.79 [0.57–0.95]. This model was integrated into an interactive online tool (<a href="https://grady-bcvi-calc.shinyapps.io/calculator/">https://grady-bcvi-calc.shinyapps.io/calculator/</a>), where patient demographic and injury characteristics can be used to compute baseline stroke risk and estimate stroke risk with AT.</p> Conclusion <p>We developed and evaluated a preliminary predictive model for personalized stroke risk assessment in patients with BCVI using key risk factors. The integration of patient-specific risk-benefit assessments into clinical decision-making could optimize and reduce variability in AT therapy. External validation is warranted to prepare this tool for broad clinical applicability.</p>

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A machine-learning-based tool for stroke risk prediction in blunt cerebrovascular injury: development and preliminary evaluation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Victoria Wagner,
  • Joshua D. Preston,
  • Alejandro De Leon Castro,
  • William F. Mueller,
  • Jonathan Nguyen,
  • Manuel Garcia-Toca,
  • Elizabeth R. Benjamin,
  • S. Rob Todd,
  • Jason D. Sciarretta

摘要

Purpose

Stroke risk correlates with the Biffl grading system in blunt cerebrovascular injury (BCVI). Although anti-thrombotic therapy is the mainstay of stroke prevention, no point-of-care clinical decision-support tool exists to guide timing for therapy. We sought to develop an interactive online calculator that incorporates patient-specific demographic and injury characteristics to estimate stroke risk and risk reduction with anti-thrombotic (AT) administration.

Methods

Data from BCVI patients (n = 1,197) at a Level I Trauma Center were retrospectively collected. Six machine learning methods were employed to predict stroke risk with and without AT therapy. Class imbalance was addressed using downsampling and/or class weighting. Model performance was assessed using 10-fold cross-validation. The model was implemented as an R-based Shiny online application.

Results

Stroke rate among the population was 4%, and the strongest predictors for stroke were the greatest Biffl grade of carotid (aOR [95%CI] = 2.02 [1.62–2.53]) and vertebral injuries (1.44 [1.18–1.77]). The least absolute shrinkage and selection operator (LASSO) model outperformed all others, achieving 66% [33%–100%] sensitivity and 74% [62%–82%] specificity for stroke prediction, with an area under the receiver operating characteristic curve of 0.79 [0.57–0.95]. This model was integrated into an interactive online tool (https://grady-bcvi-calc.shinyapps.io/calculator/), where patient demographic and injury characteristics can be used to compute baseline stroke risk and estimate stroke risk with AT.

Conclusion

We developed and evaluated a preliminary predictive model for personalized stroke risk assessment in patients with BCVI using key risk factors. The integration of patient-specific risk-benefit assessments into clinical decision-making could optimize and reduce variability in AT therapy. External validation is warranted to prepare this tool for broad clinical applicability.