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

NCT05385874 | Completed

• 1 other identifier: SHOUFA2020-3-4114
• Study Type: observational
• Target: 13,228
• Locations: 1 country
• Sites: 1

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

Outcome Measures

Primary Outcomes (1)

• AUC

  the AUC of the prediciton model based on the test data

  an average of 3 years after baseline assessement

Secondary Outcomes (4)

• sensitivity

  an average of 3 years after baseline assessement

• specificity

  an average of 3 years after baseline assessement

• positive predictive value

  an average of 3 years after baseline assessement

• negative predictive value

  an average of 3 years after baseline assessement

Study Arms (2)

Training cohort

The training cohort will be used for model development.

Testing cohort

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

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population of this project was those community-dwelling older adults with normal cognitive function at baseline and completed cognitive function assessment three years later.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Peking University Sixth Hospital: lead

Study Sites (1)

Peking University Six Hospital

Beijing, 100191, China

Location

MeSH Terms

Conditions

Cognitive Dysfunction

Condition Hierarchy (Ancestors)

Cognition Disorders; Neurocognitive Disorders; Mental Disorders

Study Officials

• Feifei Gao, Ph.D

  Peking University Six Hospital

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Researcher

Study Record Dates

• First Submitted: May 11, 2022
• First Posted: May 23, 2022
• Study Start: April 1, 2022
• Primary Completion: December 30, 2023
• Study Completion: December 30, 2023
• Last Updated: April 4, 2024

Record last verified: 2024-04

Locations

CN (1)