Open AccessDevelopment of a dementia prediction model for primary care: The Hisayama Study
Author(s) -
Honda Takanori,
Ohara Tomoyuki,
Yoshida Daigo,
Shibata Mao,
Ishida Yuki,
Furuta Yoshihiko,
Oishi Emi,
Hirakawa Yoichiro,
Sakata Satoko,
Hata Jun,
Nakao Tomohiro,
Ninomiya Toshiharu
Publication year - 2021
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1002/dad2.12221
Subject(s) - dementia , medicine , proportional hazards model , statistic , primary care , stroke (engine) , calibration , diabetes mellitus , statistics , mathematics , disease , family medicine , mechanical engineering , engineering , endocrinology
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.