Machine learning and conventional Cox regression to predict target-lesion revascularization after percutaneous coronary intervention - Takeaways - MDSpire
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Comparative Analysis of Machine Learning Techniques and Traditional Cox Regression for Predicting Target-Lesion Revascularization Following Percutaneous Coronary Intervention

  • By

  • Mona El-Faramawi

  • Marco Busco

  • Sören Möller

  • Lisette Okkels Jensen

  • Jens Flensted Lassen

  • July 1, 2026

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  • 1

    The study analyzed 24,360 patients with 34,149 lesions treated with PCI to predict target-lesion revascularization (TLR) using machine learning and Cox regression.

  • 2

    Machine learning-based Cox-LASSO showed a minor improvement in predicting short-term TLR risk compared to conventional Cox regression models.

  • 3

    At 1–5 years post-PCI, stepwise Cox regression demonstrated the best predictive performance for TLR, outperforming Cox-LASSO.

  • 4

    Most risk factors for TLR were consistent between conventional Cox models and the Cox-LASSO model, indicating similar predictive capabilities.

  • 5

    The study concluded that Cox-LASSO did not significantly enhance predictive performance over traditional Cox regression for TLR.

Original Source(s)

Machine learning and conventional Cox regression to predict target-lesion revascularization after percutaneous coronary intervention

  • by Mona El-Faramawi, Marco Busco, Sören Möller, Lisette Okkels Jensen, Jens Flensted Lassen

  • July 1, 2026

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