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Development and validation of a frailty risk model for patients with mild cognitive impairment
Yuyu Cui1, Zhening Xu1, Zhaoshu Cui1
1School of Medicine, Yan'an University, Yan'an, 716000, China.
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.
Insights
A new nomogram effectively predicts frailty risk in individuals with mild cognitive impairment (MCI). Key factors include age, ADL score, depression, grip strength, and chronic conditions, aiding early intervention for better health outcomes.
Area of Science:
- Gerontology
- Neurology
- Public Health
Background:
- Mild cognitive impairment (MCI) is associated with an increased risk of frailty.
- Early identification of frailty in MCI patients is crucial for timely interventions.
- Existing frailty prediction models may not be optimized for the MCI population.
Purpose of the Study:
- To develop and validate a predictive model for frailty risk in individuals with MCI.
- To identify key predictors of frailty in the MCI population.
- To establish a user-friendly nomogram for clinical application.
Main Methods:
- Cross-sectional analysis of nationally representative CHARLS data (2013-2015).
- Development of a nomogram using LASSO and multivariable logistic regression.
- Validation of the nomogram using ROC curves, calibration curves, and decision curve analysis.
Main Results:
- A total of 3,196 MCI patients were analyzed; 25.1% exhibited frailty.
- Independent predictors identified: age, ADL score, depression, grip strength, CVD, liver disease, pain, hearing, and vision.
- The nomogram demonstrated robust predictive performance with AUCs of 0.810 (training) and 0.791 (validation).
Conclusions:
- The developed nomogram is an effective tool for predicting frailty in MCI patients.
- The model facilitates early clinical interventions, potentially reducing frailty incidence.
- Improved frailty prediction can enhance long-term health outcomes for individuals with MCI.
