Development and validation of a frailty risk model for patients with mild cognitive impairment
Abstract The study aims to develop and validate an effective model for predicting frailty risk in individuals with mild cognitive impairment (MCI). The cross-sectional analysis employed nationally representative data from CHARLS 2013–2015. The sample was randomly divided into training (70%) and vali...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-88275-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571755301961728 |
|---|---|
| author | Yuyu Cui Zhening Xu Zhaoshu Cui Yuanyuan Guo Peiwei Wu Xiaoyan Zhou |
| author_facet | Yuyu Cui Zhening Xu Zhaoshu Cui Yuanyuan Guo Peiwei Wu Xiaoyan Zhou |
| author_sort | Yuyu Cui |
| collection | DOAJ |
| description | Abstract The study aims to develop and validate an effective model for predicting frailty risk in individuals with mild cognitive impairment (MCI). The cross-sectional analysis employed nationally representative data from CHARLS 2013–2015. The sample was randomly divided into training (70%) and validation sets (30%). The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression model were used to identify independent predictors and establish a nomogram to predict the occurrence of frailty. The receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) were used to evaluate the performance of the nomogram. A total of 3,196 MCI patients were analyzed, and 803 (25.1%) exhibited symptoms of frailty. Multivariate logistic regression analysis revealed that age, activities of daily living (ADL) score, depression score, grip strength, cardiovascular disease (CVD), liver disease, pain, hearing, and vision were associated factors for frailty in MCI patients. The nomogram based on these factors achieved AUC values of 0.810 (95% CI 0.780, 0.840) in the training set and 0.791 (95% CI 0.760, 0.820) in the validation set. Calibration curves showed good agreement between the nomogram and the observed values. The Hosmer-Lemeshow test results for the training and validation sets were P = 0.396 and P = 0.518, respectively. The ROC curve and decision curve analysis further validated the robust predictive ability of the nomogram. The application of this model may facilitate early clinical interventions, thereby potentially reducing the incidence of frailty among patients with MCI and significantly enhancing their long-term health outcomes. |
| format | Article |
| id | doaj-art-ca6c99b7059c4c0a80139270c98e3bf2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ca6c99b7059c4c0a80139270c98e3bf22025-02-02T12:20:27ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-88275-yDevelopment and validation of a frailty risk model for patients with mild cognitive impairmentYuyu Cui0Zhening Xu1Zhaoshu Cui2Yuanyuan Guo3Peiwei Wu4Xiaoyan Zhou5School of Medicine, Yan’an UniversitySchool of Medicine, Yan’an UniversitySchool of Medicine, Yan’an UniversitySchool of Medicine, Yan’an UniversitySchool of Medicine, Yan’an UniversitySchool of Medicine, Yan’an UniversityAbstract The study aims to develop and validate an effective model for predicting frailty risk in individuals with mild cognitive impairment (MCI). The cross-sectional analysis employed nationally representative data from CHARLS 2013–2015. The sample was randomly divided into training (70%) and validation sets (30%). The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression model were used to identify independent predictors and establish a nomogram to predict the occurrence of frailty. The receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) were used to evaluate the performance of the nomogram. A total of 3,196 MCI patients were analyzed, and 803 (25.1%) exhibited symptoms of frailty. Multivariate logistic regression analysis revealed that age, activities of daily living (ADL) score, depression score, grip strength, cardiovascular disease (CVD), liver disease, pain, hearing, and vision were associated factors for frailty in MCI patients. The nomogram based on these factors achieved AUC values of 0.810 (95% CI 0.780, 0.840) in the training set and 0.791 (95% CI 0.760, 0.820) in the validation set. Calibration curves showed good agreement between the nomogram and the observed values. The Hosmer-Lemeshow test results for the training and validation sets were P = 0.396 and P = 0.518, respectively. The ROC curve and decision curve analysis further validated the robust predictive ability of the nomogram. The application of this model may facilitate early clinical interventions, thereby potentially reducing the incidence of frailty among patients with MCI and significantly enhancing their long-term health outcomes.https://doi.org/10.1038/s41598-025-88275-yPredictive modelFrailtyMild cognitive impairmentCHARLS |
| spellingShingle | Yuyu Cui Zhening Xu Zhaoshu Cui Yuanyuan Guo Peiwei Wu Xiaoyan Zhou Development and validation of a frailty risk model for patients with mild cognitive impairment Scientific Reports Predictive model Frailty Mild cognitive impairment CHARLS |
| title | Development and validation of a frailty risk model for patients with mild cognitive impairment |
| title_full | Development and validation of a frailty risk model for patients with mild cognitive impairment |
| title_fullStr | Development and validation of a frailty risk model for patients with mild cognitive impairment |
| title_full_unstemmed | Development and validation of a frailty risk model for patients with mild cognitive impairment |
| title_short | Development and validation of a frailty risk model for patients with mild cognitive impairment |
| title_sort | development and validation of a frailty risk model for patients with mild cognitive impairment |
| topic | Predictive model Frailty Mild cognitive impairment CHARLS |
| url | https://doi.org/10.1038/s41598-025-88275-y |
| work_keys_str_mv | AT yuyucui developmentandvalidationofafrailtyriskmodelforpatientswithmildcognitiveimpairment AT zheningxu developmentandvalidationofafrailtyriskmodelforpatientswithmildcognitiveimpairment AT zhaoshucui developmentandvalidationofafrailtyriskmodelforpatientswithmildcognitiveimpairment AT yuanyuanguo developmentandvalidationofafrailtyriskmodelforpatientswithmildcognitiveimpairment AT peiweiwu developmentandvalidationofafrailtyriskmodelforpatientswithmildcognitiveimpairment AT xiaoyanzhou developmentandvalidationofafrailtyriskmodelforpatientswithmildcognitiveimpairment |