What is the clinical evidence from this study?

Study design: Cross-Sectional. Population: Cardiovascular disease (n=1184). Intervention: Nomogram prediction model. Primary outcome: Frailty risk prediction (Model discrimination) (AUC 0.861, 95% CI 0.804-0.917).

What does this research mean for the field?

A nomogram model incorporating sleep duration, activities of daily living, waist circumference, cognitive function, handgrip strength, age, and depression effectively predicts frailty risk in middle-aged and older adults with cardiovascular disease. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to create and validate a prediction model for frailty risk specifically in middle-aged and older patients with cardiovascular disease.

March 18, 2026Open Access

Prediction Model for Frailty in Middle-Aged and Older Adults with Cardiovascular Disease

Resultado clave

A nomogram incorporating seven clinical variables accurately predicted frailty risk in middle-aged and older adults with cardiovascular disease, achieving an AUC of 0.861 in the validation cohort.

Puntos clave

This study aims to create and validate a prediction model for frailty risk specifically in middle-aged and older patients with cardiovascular disease.
Conducted a cross-sectional study using data from CHARLS 2015.
Included 1184 participants aged 45 and older with cardiovascular disease.
Divided participants into training and validation cohorts in a 7:3 ratio.
Utilized LASSO regression for variable selection followed by multivariable logistic regression to create a nomogram.
Evaluated model performance with ROC curves, calibration plots, the Hosmer–Lemeshow test, and decision curve analysis.
Frailty identified in 148 participants (12.5%).
Independent predictors of frailty include sleep duration, ADL, waist circumference, cognitive function, handgrip strength, age, and depression.
Nomogram showed good discrimination with AUC values of 0.851 and 0.861 for training and validation cohorts, respectively.
Calibration demonstrated good agreement (P>0.05 from Hosmer–Lemeshow test).
Decision curve analysis indicated favorable clinical utility for the model.

Diseño del estudio

Tipo

Cross-Sectional (n=1,184)

Multicéntrico

Sí

PICO estructurado

Does a clinical nomogram accurately predict frailty risk in middle-aged and older adults with cardiovascular disease?

Población

1,184 middle-aged and older adults (age ≥ 45 years) with cardiovascular disease (self-reported physician diagnosis of heart attack, coronary heart disease, angina, congestive heart failure, or stroke) from the 2015 China Health and Retirement Longitudinal Study (CHARLS). Mean age 62.8 years, 86.6% female.

Intervención

Development and validation of a nomogram prediction model for frailty risk incorporating seven independent predictors: sleep duration, activities of daily living (ADL), waist circumference, cognitive function, handgrip strength, age, and depression.

Resultado

Frailty, defined by a modified Fried frailty phenotype (score ≥3 across 5 domains: weakness, exhaustion, slowness, low physical activity, and weight loss).composite

A newly developed nomogram using seven easily obtainable clinical variables can accurately predict frailty risk in middle-aged and older patients with cardiovascular disease, facilitating early screening.

Resultado numérico

Estimación del efecto: AUC 0.861 (95% CI 0.804-0.917)

Limitaciones

Diagnosis of CVD was based on self-reported physician diagnoses, which may be subject to recall bias.
Potential confounding factors such as dietary patterns, medication use, and inflammatory biomarkers were unavailable.
Cross-sectional design prevents inferring causal relationships.
External validation of the model has not yet been performed.

Resumen

Background: Frailty is common among patients with cardiovascular disease (CVD) and is associated with adverse clinical outcomes. However, practical tools for predicting frailty risk in middle-aged and older patients with CVD remain limited. This study aimed to develop and validate a prediction model for frailty risk in patients with CVD. Methods: A cross-sectional study was conducted using data from the 2015 China Health and Retirement Longitudinal Study (CHARLS). A total of 1184 participants aged ≥ 45 years with CVD were included and randomly divided into training and validation cohorts at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by multivariable logistic regression to construct a nomogram model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, the Hosmer–Lemeshow test, and decision curve analysis (DCA). Results: Frailty was identified in 148 participants (12.5%). Sleep duration, activities of daily living (ADL), waist circumference, cognitive function, handgrip strength, age, and depression were independent predictors of frailty. The nomogram demonstrated good discrimination, with area under the curve (AUC) values of 0.851 (95% CI: 0.814– 0.888) in the training cohort and 0.861 (95% CI: 0.804– 0.917) in the validation cohort. Calibration showed good agreement between predicted and observed outcomes (Hosmer–Lemeshow test, P> 0.05). DCA indicated favorable clinical utility. Conclusion: This nomogram provides a simple and effective tool for predicting frailty risk in patients with CVD and may facilitate early screening and risk stratification in clinical practice. Keywords: predictive model, frailty, cardiovascular disease, CHARLS

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Cite This Study

Yang et al. (Sun,) conducted a cross-sectional in Cardiovascular disease (n=1,184). Nomogram prediction model was evaluated on Frailty risk prediction (Model discrimination) (AUC 0.861, 95% CI 0.804-0.917). A nomogram incorporating seven clinical variables accurately predicted frailty risk in middle-aged and older adults with cardiovascular disease, achieving an AUC of 0.861 in the validation cohort.

synapsesocial.com/papers/69ba43984e9516ffd37a4eeb https://doi.org/https://doi.org/10.2147/vhrm.s581066

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