ArticleFrontiers in public health2025
Development and validation of a predictive model for frailty risk in older adults with cardiovascular-metabolic comorbidities.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline.Cell reports methods · 2026Article
- In the shadow of stability lies ruin: Occult vascular injuries in geriatric pelvic trauma.World journal of clinical cases · 2025Article
- Development and validation of a model to predict the risk of frailty in older adults with panvascular disease.Frontiers in public health · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: With the rapid progression of population aging, the number of frail individuals is steadily rising, making frailty a pressing public health issue that demands urgent attention. Compared to individuals with a single cardiovascular-metabolic disease, patients with cardiovascular-metabolic multimorbidity (CMM) are more prone to developing frailty. This study aimed to develop and validate a predictive model for assessing frailty risk in older adult patients with CMM. Methods: The data came from participants in the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS). The study population comprised individuals aged 60 years and older with CMM and complete frailty scale measurements. Frailty status was evaluated using the Fried Frailty Scale. 26 indicators, including socio-demographic characteristics, lifestyle factors, overall health condition, and psychological well-being. The entire sample was randomly allocated into training and validation sets at a 7:3 ratio. LASSO regression and logistic regression was conducted to evaluate factors associated with frailty. A nomogram was constructed using the identified predictors to predict outcomes. The discrimination, accuracy, and clinical effectiveness of the model were evaluated by the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA). Results: The study included 2,164 older adult CMM participants, with 387 (17.88%) displaying frailty symptoms. Binary logistic regression analyses revealed that depression, social activity, history of falls, life satisfaction, ADL scores, cognitive function, age and the number of CMDs were significantly associated with frailty. These eight factors were incorporated into the nomogram model, and the AUC values for the predictive model were 0.816 (95% CI = 0.787-0.848) and 0.816 (95% CI = 0.786-0.846) for the training and validation sets, respectively, indicating effective discrimination. Hosmer-Lemeshow test results showed Conclusion: The nomogram prediction model developed in this research is a reliable and effective tool for assisting clinicians in identifying frailty in older adult CMM patients at an early stage, providing a scientific foundation for individualized health management and intervention.
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