• Development and validation of...

Treffer: Development and validation of a predictive model for cognitive frailty in community-dwelling older adults: a cross-sectional study.

Title:
Development and validation of a predictive model for cognitive frailty in community-dwelling older adults: a cross-sectional study.
Authors:
Sun L; School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China.; School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China., Zhang Y; School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China.; School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China., Ji W; School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China.; School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China., Zheng J; School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China.; School of Nursing, Shanghai University of Traditional Chinese Medicine, Shanghai, China., Zheng G; School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China., Xia J; School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Source:
Frontiers in public health [Front Public Health] 2025 Dec 19; Vol. 13, pp. 1673370. Date of Electronic Publication: 2025 Dec 19 (Print Publication: 2025).
Publication Type:
Evaluation Study; Journal Article
Language:
English
Journal Info:
Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE
Imprint Name(s):
Original Publication: Lausanne : Frontiers Editorial Office
MeSH Terms:
Aging in Place*/psychology , Cognitive Dysfunction*/diagnosis , Frailty*/diagnosis , Frailty*/psychology , Geriatric Assessment*/methods , Predictive Learning Models*/standards, Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cross-Sectional Studies ; Decision Support Techniques ; Frail Elderly/psychology ; Logistic Models ; Predictive Value of Tests ; ROC Curve ; Software Validation ; Syndrome ; China
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Contributed Indexing:
Keywords: cognitive frailty; cognitive frailty screening; community-dwelling older adults; cross-sectional study; prediction model
Entry Date(s):
Date Created: 20260105 Date Completed: 20260116 Latest Revision: 20260116
Update Code:
20260116
PubMed Central ID:
PMC12757231
DOI:
10.3389/fpubh.2025.1673370
PMID:
41487637
Database:
MEDLINE

Weitere Informationen

Background: Cognitive frailty (CF) is a geriatric syndrome involving both physical frailty and cognitive impairment, increasing vulnerability to adverse health outcomes. However, practical prediction models integrating easily accessible, modifiable risk factors for community settings are lacking. This study aimed to develop and validate a predictive model for CF in community-dwelling older adults.
Methods: A cross-sectional study was conducted from September 2022 to May 2024 in Pudong New District, Shanghai, with 979 participants aged 60 and above. Data on sociodemographic characteristics, behavioral factors, nutritional status, sleep quality, depression, as well as lifestyle, health-related factors, physical frailty, and cognitive function were collected through questionnaires administered to older adults, with 20 indicators analyzed. The population was divided into a 7:3 ratio for training and validation. LASSO regression and multivariate logistic regression identified risk factors, and a nomogram prediction model was developed. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).
Results: Of the 979 participants, 31.1% were diagnosed with CF. Seven predictors, including marital status, smoking, Timed Up and Go test (TUGT), depression, sleep quality, nutrition, and medication count, were identified to construct the model. Together, these variables provide a comprehensive assessment of the risk of cognitive frailty in older adults. The model exhibited good predictive performance, with AUC values of 0.753 and 0.733 for the development and validation sets, respectively. The p -values for the Hosmer-Lemeshow test were 0.507 and 0.537 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curves also show that the model has good predictive ability and stability.
Conclusion: Community-dwelling older adults have a higher incidence of cognitive frailty. This study developed an effective, low-cost, and non-invasive model with promising predictive capabilities that can be used as a screening tool to identify community-dwelling older adults at high risk for cognitive frailty in clinical practice. This model is expected to assist healthcare professionals in improving the effectiveness of prevention of cognitive frailty in community-dwelling older adults.
(Copyright © 2025 Sun, Zhang, Ji, Zheng, Zheng and Xia.)

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.