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Machine learning improves mortality prediction in three-vessel disease

  • Author Footnotes
    1 These authors contributed equally to the study.
    Xinxing Feng
    Footnotes
    1 These authors contributed equally to the study.
    Affiliations
    Endocrinology and Cardiovascular Disease Centre, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Author Footnotes
    1 These authors contributed equally to the study.
    Ce Zhang
    Footnotes
    1 These authors contributed equally to the study.
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China

    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Author Footnotes
    1 These authors contributed equally to the study.
    Xin Huang
    Footnotes
    1 These authors contributed equally to the study.
    Affiliations
    Solar Activity Prediction Center, National Astronomical Observatories, Chinese Academy of Sciences, China

    Nanjing TooBoo Technology Co., Ltd, China
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  • Junhao Liu
    Affiliations
    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Lin Jiang
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Lianjun Xu
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Jian Tian
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Xueyan Zhao
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Dong Wang
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Yin Zhang
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Kai Sun
    Affiliations
    Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Bo Xu
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Wei Zhao
    Affiliations
    Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Rutai Hui
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China

    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Runlin Gao
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Jinqing Yuan
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China

    National Clinical Research Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Jizheng Wang
    Correspondence
    Corresponding author.
    Affiliations
    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
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  • Yanfeng Duan
    Correspondence
    Corresponding author. Nanjing TooBoo Technology Co., Ltd, China.
    Affiliations
    Nanjing TooBoo Technology Co., Ltd, China
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  • Lei Song
    Correspondence
    Corresponding author. Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China.
    Affiliations
    Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China

    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China

    National Clinical Research Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, China
    Search for articles by this author
  • Author Footnotes
    1 These authors contributed equally to the study.

      Highlights

      • Machine learning (ML) can address the limitations of regression-based models.
      • The ML-derived model exhibited better predictions than an established model.
      • The ML-based approach can be used for risk stratification in clinical practice.

      Abstract

      Background and aims

      Risk stratification for three-vessel coronary artery disease (3VD) remains an important clinical challenge. In this study, we utilized machine learning (ML), which can address the limitations of traditional regression-based models, to develop a novel model to assess mortality risk in patients with 3VD.

      Methods

      This study was based on a prospective cohort of 8943 participants with 3VD consecutively enrolled between 2004 and 2011. An ML-derived random forest model was trained and tested to predict 4-year mortality. The predictability of the model was compared with that of an established model, the Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery score II (SSII), among 3VD patients undergoing percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and medical therapy (MT) alone.

      Results

      The all-cause mortality was 7.5% (667 patients) over the 4-year follow-up period. The correlation-based feature selection algorithm selected 18 of the 94 features to develop the ML model. In the testing dataset, the ML-derived model achieved an area under the curve of 0.81 for 4-year mortality prediction. Its predictability was significantly better than that of the SSII among patients undergoing PCI (0.80 vs. 0.70, p < 0.001) or CABG (0.80 vs. 0.67, p < 0.001). The model also outperformed the SSII in patients receiving MT alone (ML: 0.75 vs. SSII for PCI: 0.70 or SSII for CABG: 0.66, p < 0.001).

      Conclusions

      This ML-based approach exhibited better performance in risk stratification for 3VD compared with the conventional method. Further validation studies are needed to confirm these findings.

      Graphical abstract

      Keywords

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