Aging, Cognitive Impairment, Cohort, Predictive Model
Conditions
Brief summary
Cognitive impairment is one of the core early signs of dementia, and it is also a key stage for community-based dementia prevention. Accurate and convenient prediction of cognitive impairment can help the community to identify and manage the high-risk population of dementia. Previous studies had developed several dementia predicting models, but such models may be not suitable for cognitive impairment prediction. Based on the national representative follow-up data of Chinese Longitudinal Healthy Longevity Survey (CLHLS), this project aims to develop and validate a brief cognitive impairment prediction algorithm among the community-dwelling elderly, using machine learning methods (such as Logistic regression, Naïve Bayes model, Extreme Gradient Boosting Tree and so on). Finally, based on the constructed model, an easy-to-use online intelligent assessment tool for predicting cognitive impairment risk will be developed. The general practitioners, social workers and the elderly would be invited to use the tool and we will revise the tool according to their suggestions and comments. This project is expected to provide scientific basis and technical support for community-based dementia prevention, and will also be useful for the elderly to easily understand their cognitive health.
Interventions
None listed
Sponsors
Peking University Sixth Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Aged 65 or over at baseline; 2. With normal cognitive function at baseline (score ≥ 18 on the Chinese version of Mini-Mental State Examination, MMSE); 3. Completed MMSE assessment three years later; 4. Provided informed consent voluntarily.
Exclusion criteria
1. Aged \<65; 2. had a history of dementia or MMSE score \< 18 at baseline; 3. lost to follow-up or without cognitive function assessment three years later; 4. Refused to participate the survey.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC | an average of 3 years after baseline assessement | the AUC of the prediciton model based on the test data |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| positive predictive value | an average of 3 years after baseline assessement | the positive predictive value of the prediciton model based on the test data |
| negative predictive value | an average of 3 years after baseline assessement | the negative predictive value of the prediciton model based on the test data |
| sensitivity | an average of 3 years after baseline assessement | the sensitivity of the prediciton model based on the test data |
| specificity | an average of 3 years after baseline assessement | the specificity of the prediciton model based on the test data |
Countries
China
Outcome results
None listed