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Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.

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Summary

A new machine learning model accurately predicts ischemic stroke (IS) risk after percutaneous coronary intervention (PCI). This random forest model outperforms traditional logistic regression, aiding in better patient management and reducing stroke incidence.

Keywords:
ischemic strokeoutcomemachine learningpercutaneous coronary intervention

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Area of Science:

  • Cardiology
  • Neurology
  • Machine Learning in Medicine

Background:

  • Ischemic stroke (IS) is a severe complication following percutaneous coronary intervention (PCI).
  • Current risk prediction models for post-PCI IS are lacking.
  • This complication leads to significant morbidity and economic burden.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting IS after PCI.
  • To compare the performance of an ML model against traditional logistic regression.

Main Methods:

  • Utilized data from the Mayo Clinic CathPCI registry (2003-2018).
  • Developed a random forest (RF) model and a logistic regression (LR) model.
  • Assessed model performance using receiver operator characteristic (ROC) analysis at multiple time points (6 months to 5 years).

Main Results:

  • Included 17,356 patients; 1.5% experienced IS within 5 years post-PCI.
  • The RF model demonstrated superior predictive accuracy compared to the LR model.
  • Periprocedural stroke was identified as the strongest predictor of subsequent IS.

Conclusions:

  • The developed RF model accurately predicts both short- and long-term IS risk post-PCI.
  • Machine learning offers improved prediction over logistic regression for post-PCI IS.
  • Early identification and aggressive management of periprocedural stroke may mitigate future IS risk.