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Takanori Honda Last modified date:2019.06.13



Undergraduate School


E-Mail
Phone
092-642-6151
Fax
092-642-4854
Academic Degree
PhD
Field of Specialization
Epidemiology, Public Health, Physical activity, Behavioral Science
ORCID(Open Researcher and Contributor ID)
0000-0002-1011-9879
Research
Research Interests
  • Physical activity epidemiology on diabetes and metabolic diseases
    keyword : physical activity; epidemiology
    2015.04.
  • Nutritional epidemiology
    keyword : epidemiology
    2018.04.
  • Effects of physical activity on cognitive health
    keyword : epidemiology
    2017.12.
Academic Activities
Papers
1. Honda T, Yoshida D, Hata J, Hirakawa Y, Ishida Y, Shibata M, Sakata S, Kitazono T, Ninomiya Toshiharu, Development and validation of modified risk prediction models for cardiovascular disease and its subtypes: The Hisayama Study, Atherosclerosis, 10.1016/j.atherosclerosis.2018.10.014, 85, 38-44, 2018.10, 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 2,462 community residents aged 40-84 were followed up for 24 years. A Cox’s 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 for 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 in understanding individual risk and setting personalized goals, and in promoting risk stratification in public health strategies for CVD prevention..