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

NCT05385874 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 13228

Last updated 2024-04-04

No results posted yet for this study

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

Sponsors & Collaborators

  • Peking University Sixth Hospital

    lead OTHER

Principal Investigators

  • Feifei Gao, Ph.D · Peking University Six Hospital

Eligibility

Min Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-04-01
Primary Completion
2023-12-30
Completion
2023-12-30

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05385874 on ClinicalTrials.gov