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Development and validation of modified risk prediction models for cardiovascular disease and its subtypes: The Hisayama Study

  • Takanori Honda
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Daigo Yoshida
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Jun Hata
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Yoichiro Hirakawa
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Yuki Ishida
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Mao Shibata
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Satoko Sakata
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Takanari Kitazono
    Affiliations
    Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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  • Toshiharu Ninomiya
    Correspondence
    Corresponding author. 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Japan.
    Affiliations
    Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan

    Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan
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      Highlights

      • We developed risk prediction models for cardiovascular disease and its subtypes.
      • Our risk prediction models exhibited good performance and internal validity.
      • Predicted risk-based decision-making can be beneficial in primary prevention.

      Abstract

      Background and aims

      Predicting cardiovascular events is of practical benefit for disease prevention. The aim of this study was to develop and evaluate an updated risk prediction model for cardiovascular diseases and its subtypes.

      Methods

      A total of 2462 community residents aged 40–84 years were followed up for 24 years. A Cox proportional hazards regression model was used to develop risk prediction models for cardiovascular diseases, and separately for stroke and coronary heart diseases. The risk assessment ability of the developed model was evaluated, and a bootstrapping method was used for internal validation. The predicted risk was translated into a simplified scoring system. A decision curve analysis was used to evaluate clinical usefulness.

      Results

      The multivariable model for cardiovascular diseases included age, sex, systolic blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, smoking habits, and regular exercise as predictors. The models for stroke and coronary heart diseases incorporated both shared and unique variables. The developed models showed good discrimination with little evidence of overfitting (optimism-corrected Harrell's C statistics 0.726–0.777) and calibrations (Hosmer-Lemeshow test, p = 0.44–0.90). The decision curve analysis revealed that the predicted risk-based decision-making would have higher net benefit than either a CVD intervention strategy for all individuals or no individuals.

      Conclusions

      The developed risk prediction models showed a good performance and satisfactory internal validity, which may help understand individual risk and setting personalized goals, and promote risk stratification in public health strategies for CVD prevention.

      Keywords

      Abbreviations:

      BMI (body mass index), CVD (cardiovascular diseases), CHD (coronary heart diseases), HDL cholesterol (high-density lipoprotein cholesterol), IRE (individual risk estimates), LDL cholesterol (low-density lipoprotein cholesterol), SD (standard deviation), SE (standard error)
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