TY - JOUR
T1 - Development of a dementia prediction model for primary care
T2 - The Hisayama Study
AU - Honda, Takanori
AU - Ohara, Tomoyuki
AU - Yoshida, Daigo
AU - Shibata, Mao
AU - Ishida, Yuki
AU - Furuta, Yoshihiko
AU - Oishi, Emi
AU - Hirakawa, Yoichiro
AU - Sakata, Satoko
AU - Hata, Jun
AU - Nakao, Tomohiro
AU - Ninomiya, Toshiharu
N1 - Funding Information:
The authors thank the residents of the town of Hisayama for their participation in the survey and the staff of the Division of Health of Hisayama for their cooperation with this study. The statistical analyses were carried out using the computer resources offered under the category of General Projects by the Research Institute for Information Technology, Kyushu University. We would like to thank KN International for English proofreading. This study was supported in part by Grants‐in‐Aid for Scientific Research A (JP16H02692), B (JP17H04126, JP18H02737, JP19H03863), and C (JP18K07565, JP18K09412, JP19K07890, JP20K10503, JP20K11020), and by Grants‐in‐Aid for Early‐Career Scientists (JP18K17925) and Research Activity Start‐up (JP19K23971) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; by Health and Labor Sciences Research Grants of the Ministry of Health, Labor and Welfare of Japan (20FA1002); and by the Japan Agency for Medical Research and Development (JP20dk0207025, JP20km0405202, JP20fk0108075). In addition, this study was sponsored by DeSC Co., Ltd. (Tokyo). The funders had no role in the design and conduct of the study; the collection, analysis, and interpretation of the data; the preparation or review of the manuscript; or the decision to submit the manuscript for publication.
Funding Information:
The authors thank the residents of the town of Hisayama for their participation in the survey and the staff of the Division of Health of Hisayama for their cooperation with this study. The statistical analyses were carried out using the computer resources offered under the category of General Projects by the Research Institute for Information Technology, Kyushu University. We would like to thank KN International for English proofreading. This study was supported in part by Grants-in-Aid for Scientific Research A (JP16H02692), B (JP17H04126, JP18H02737, JP19H03863), and C (JP18K07565, JP18K09412, JP19K07890, JP20K10503, JP20K11020), and by Grants-in-Aid for Early-Career Scientists (JP18K17925) and Research Activity Start-up (JP19K23971) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; by Health and Labor Sciences Research Grants of the Ministry of Health, Labor and Welfare of Japan (20FA1002); and by the Japan Agency for Medical Research and Development (JP20dk0207025, JP20km0405202, JP20fk0108075). In addition, this study was sponsored by DeSC Co., Ltd. (Tokyo). The funders had no role in the design and conduct of the study; the collection, analysis, and interpretation of the data; the preparation or review of the manuscript; or the decision to submit the manuscript for publication.
Publisher Copyright:
© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.
PY - 2021
Y1 - 2021
N2 - Introduction: We aimed to develop a risk prediction model for incident dementia using predictors that are available in primary-care settings. Methods: A total of 795 subjects aged 65 years or over were prospectively followed-up from 1988 to 2012. A Cox proportional-hazards regression was used to develop a multivariable prediction model. The developed model was translated into a simplified scoring system based on the beta-coefficient. The discrimination of the model was assessed by Harrell's C statistic, and the calibration was assessed by a calibration plot. Results: During the follow-up period, 364 subjects developed dementia. In the multivariable model, age, female sex, low education, leanness, hypertension, diabetes, history of stroke, current smoking, and sedentariness were selected as predictors. The developed model and simplified score showed good discrimination and calibration. Discussion: The developed risk prediction model is feasible and practically useful in primary-care settings to identify individuals at high risk for future dementia.
AB - Introduction: We aimed to develop a risk prediction model for incident dementia using predictors that are available in primary-care settings. Methods: A total of 795 subjects aged 65 years or over were prospectively followed-up from 1988 to 2012. A Cox proportional-hazards regression was used to develop a multivariable prediction model. The developed model was translated into a simplified scoring system based on the beta-coefficient. The discrimination of the model was assessed by Harrell's C statistic, and the calibration was assessed by a calibration plot. Results: During the follow-up period, 364 subjects developed dementia. In the multivariable model, age, female sex, low education, leanness, hypertension, diabetes, history of stroke, current smoking, and sedentariness were selected as predictors. The developed model and simplified score showed good discrimination and calibration. Discussion: The developed risk prediction model is feasible and practically useful in primary-care settings to identify individuals at high risk for future dementia.
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U2 - 10.1002/dad2.12221
DO - 10.1002/dad2.12221
M3 - Article
AN - SCOPUS:85124396503
SN - 2352-8729
VL - 13
JO - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
JF - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
IS - 1
M1 - e12221
ER -
