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

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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.

Original languageEnglish
Pages (from-to)38-44
Number of pages7
JournalAtherosclerosis
Volume279
DOIs
Publication statusPublished - Dec 1 2018

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Cardiovascular Diseases
Decision Support Techniques
Coronary Disease
Stroke
Blood Pressure
Aptitude
Proportional Hazards Models
LDL Cholesterol
HDL Cholesterol
Calibration
Habits
Decision Making
Hemoglobins
Public Health
Smoking

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine

Cite this

@article{9debe9eb35da40f4a229186fbdabd634,
title = "Development and validation of modified risk prediction models for cardiovascular disease and its subtypes: The Hisayama Study",
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.",
author = "Takanori Honda and Daigo Yoshida and Jun Hata and Yoichiro Hirakawa and Yuki Ishida and Mao Shibata and Satoko Sakata and Takanari Kitazono and Toshiharu Ninomiya",
year = "2018",
month = "12",
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doi = "10.1016/j.atherosclerosis.2018.10.014",
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AU - Honda, Takanori

AU - Yoshida, Daigo

AU - Hata, Jun

AU - Hirakawa, Yoichiro

AU - Ishida, Yuki

AU - Shibata, Mao

AU - Sakata, Satoko

AU - Kitazono, Takanari

AU - Ninomiya, Toshiharu

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N2 - 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.

AB - 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.

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