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

NCT ID: NCT05385874

Last Updated: 2024-04-04

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 13228 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-04-01
• Study Completion Date: 2023-12-30

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.

Conditions

Cognitive Impairment; Predictive Model; Aging; Cohort

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Training cohort

The training cohort will be used for model development.

No interventions assigned to this group

Testing cohort

The testing cohort, a new cohort compared with the training cohort, will be used for model external validation.

No interventions assigned to this group

Eligibility Criteria

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.

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Peking University Sixth Hospital

OTHER

Sponsor Role: lead

Responsible Party

Xiaozhen LV

Associate Researcher

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Feifei Gao, Ph.D

Role: STUDY_DIRECTOR

Peking University Six Hospital

Locations

Peking University Six Hospital

Beijing, , China

Countries

China

Other Identifiers

SHOUFA2020-3-4114

Identifier Type: -

Identifier Source: org_study_id