Risk Prediction and Its Intelligent Assessment for Cognitive Impairment Among Community-dwelling Older Adults

Study Overview
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Brief Summary
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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.

Official Title
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Risk Prediction and Its Intelligent Assessment for Cognitive Impairment Among Community-dwelling Older Adults

Conditions
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Cognitive Impairment, Predictive Model, Aging, Cohort

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Aged 65 or over at baseline; With normal cognitive function at baseline (score ≥ 18 on the Chinese version of Mini-Mental State Examination, MMSE); Completed MMSE assessment three years later; Provided informed consent voluntarily. Exclusion Criteria: Aged <65; had a history of dementia or MMSE score < 18 at baseline; lost to follow-up or without cognitive function assessment three years later; Refused to participate the survey.

Ages Eligible for Study
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Minimum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training cohort<br>The training cohort will be used for model development. | |
| Testing cohort<br>The testing cohort, a new cohort compared with the training cohort, will be used for model external validation. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUC | the AUC of the prediciton model based on the test data | an average of 3 years after baseline assessement | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| sensitivity | the sensitivity of the prediciton model based on the test data | an average of 3 years after baseline assessement | 
| specificity | the specificity of the prediciton model based on the test data | an average of 3 years after baseline assessement | 
| positive predictive value | the positive predictive value of the prediciton model based on the test data | an average of 3 years after baseline assessement | 
| negative predictive value | the negative predictive value of the prediciton model based on the test data | an average of 3 years after baseline assessement |