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.
Keywords: dementia; general population; primary prevention; prospective study; risk prediction model.
© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.