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

Sponsor
Peking University Sixth Hospital (Other)
Overall Status
Recruiting
CT.gov ID
NCT05385874
Collaborator
(none)
15,000
1
21
715.6

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    15000 participants
    Observational Model:
    Cohort
    Time Perspective:
    Prospective
    Official Title:
    Risk Prediction and Its Intelligent Assessment for Cognitive Impairment Among Community-dwelling Older Adults
    Actual Study Start Date :
    Apr 1, 2022
    Anticipated Primary Completion Date :
    Nov 30, 2023
    Anticipated Study Completion Date :
    Dec 30, 2023

    Arms and Interventions

    Arm Intervention/Treatment
    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.

    Outcome Measures

    Primary Outcome Measures

    1. AUC [an average of 3 years after baseline assessement]

      the AUC of the prediciton model based on the test data

    Secondary Outcome Measures

    1. sensitivity [an average of 3 years after baseline assessement]

      the sensitivity of the prediciton model based on the test data

    2. specificity [an average of 3 years after baseline assessement]

      the specificity of the prediciton model based on the test data

    3. positive predictive value [an average of 3 years after baseline assessement]

      the positive predictive value of the prediciton model based on the test data

    4. negative predictive value [an average of 3 years after baseline assessement]

      the negative predictive value of the prediciton model based on the test data

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    65 Years and Older
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    Yes
    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.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Peking University Six Hospital Beijing China 100191

    Sponsors and Collaborators

    • Peking University Sixth Hospital

    Investigators

    • Study Director: Feifei Gao, Ph.D, Peking University Six Hospital

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Xiaozhen LV, Associate Researcher, Peking University Sixth Hospital
    ClinicalTrials.gov Identifier:
    NCT05385874
    Other Study ID Numbers:
    • SHOUFA2020-3-4114
    First Posted:
    May 23, 2022
    Last Update Posted:
    May 23, 2022
    Last Verified:
    May 1, 2022
    Individual Participant Data (IPD) Sharing Statement:
    Undecided
    Plan to Share IPD:
    Undecided
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Additional relevant MeSH terms:

    Study Results

    No Results Posted as of May 23, 2022