Construction and validation of a machine learning-based prediction model for venous thromboembolism in lung transplant recipients supported by ECMO - Takeaways - MDSpire

Construction and validation of a machine learning-based prediction model for venous thromboembolism in lung transplant recipients supported by ECMO

  • By

  • Yan Zhu

  • Fei Zeng

  • Mei-Juan Lan

  • Jiang-Shu-Yuan Liang

  • Ling-Yun Cai

  • Pei-Pei Gu

  • Lu-Yao Guo

  • June 4, 2026

  • 0 min

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  • 1

    The study developed a machine learning model to predict venous thromboembolism (VTE) in lung transplant patients on ECMO.

  • 2

    Data from 189 patients were analyzed using Recursive Feature Elimination to identify six predictive factors for VTE.

  • 3

    The Random Forest model demonstrated the highest performance with an AUC of 0.895 and an accuracy of 89.7%.

  • 4

    The model's predictive ability was validated through decision curve analysis, indicating significant clinical utility.

  • 5

    This research addresses the lack of validated VTE risk prediction tools for lung transplant recipients undergoing ECMO.

Original Source(s)

Construction and validation of a machine learning-based prediction model for venous thromboembolism in lung transplant recipients supported by ECMO

  • by Yan Zhu, Fei Zeng, Mei-Juan Lan, Jiang-Shu-Yuan Liang, Ling-Yun Cai, Pei-Pei Gu, Lu-Yao Guo

  • June 4, 2026

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