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

Completed

• Conditions: Predictive Model; Cohort; Cognitive Impairment; Aging

• Registration Number: NCT05385874
• Lead Sponsor: Peking University Sixth Hospital

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-04-01
• Study First Submitted: 2022-05-11
• Study First Submitted QC: 2022-05-17
• Study First Posted: 2022-05-23
• Primary Completion: 2023-12-30
• Study Completion: 2023-12-30
• Status Verified: 2024-04
• Last Update Submitted: 2024-04-02
• Last Update Posted: 2024-04-04

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 13228

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
AUC	an average of 3 years after baseline assessement	the AUC of the prediciton model based on the test data

Secondary Outcome Measures

Name	Time	Method
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
negative predictive value	an average of 3 years after baseline assessement	the negative predictive value of the prediciton model based on the test data
positive predictive value	an average of 3 years after baseline assessement	the positive predictive value of the prediciton model based on the test data

Trial Locations

Locations (1)

Peking University Six Hospital; 🇨🇳 Beijing, China