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?

To develop and validate a practical prediction model for frailty risk among middle-aged and older patients with cardiovascular disease.

March 18, 2026Vascular Health and Risk ManagementOpen Access

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

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Key result

Seven-variable nomogram accurately predicts frailty risk in older CVD patients with an AUC of ~0.86.

AUC 0.861
95% CI 0.804-0.917
n=1,184

Why the study?

Frailty is common and linked to adverse outcomes in CVD, but practical tools to predict frailty risk in middle-aged and older CVD patients remain limited.

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

Key Points

To develop and validate a practical prediction model for frailty risk among middle-aged and older patients with cardiovascular disease.
Analyzed data from the 2015 China Health and Retirement Longitudinal Study (CHARLS) comprising 1184 participants aged ≥ 45 years with cardiovascular disease, randomly allocated at a 7:3 ratio into a training set (n = 829) and a validation set (n = 355).
Selected potential predictors using LASSO regression and constructed a nomogram prediction model using multivariable logistic regression, evaluating performance via receiver operating characteristic curves, calibration plots, the Hosmer–Lemeshow test, and decision curve analysis.
Frailty was identified in 148 participants (12.5%), with significant independent predictors identified as depression (OR 4.87, 95% CI: 2.73–9.20, P < 0.001), activities of daily living score (OR 0.35, 95% CI: 0.23–0.51, P < 0.001), age (OR 1.03, 95% CI: 1.01–1.07, P = 0.038), and waist circumference (OR 0.98, 95% CI: 0.96–0.99, P = 0.027).
Cognition score (OR 0.91, 95% CI: 0.85–0.95, P < 0.001), sleep duration (OR 0.89, 95% CI: 0.79–0.99, P = 0.046), and grip strength (OR 0.96, 95% CI: 0.93–0.99, P = 0.038) were also independent predictors of frailty.
The nomogram exhibited good discrimination with an AUC of 0.851 (95% CI: 0.814–0.888) in the training set and 0.861 (95% CI: 0.804–0.917) in the validation set, showing adequate calibration by Hosmer–Lemeshow testing (training P = 0.5724; validation P = 0.3294).

Study Design

Type

Cross-Sectional (n=1,184)

Multicenter

Yes

Structured PICO

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

Population

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.

Intervention

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.

Outcome

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

Main Result

Effect estimate: AUC 0.861 (95% CI 0.804-0.917)

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.

Limitations

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.

Implication

May enable early frailty screening in CVD patients; hypothesis-generating and requires prospective validation before routine adoption.

Abstract

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.

Discussion

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Published by Vascular Health and Risk Management · CC BY-NC

Cite This Study

Yang et al. (2026) 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/10.2147/vhrm.s581066

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Implication

May enable early frailty screening in CVD patients; hypothesis-generating and requires prospective validation before routine adoption.

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

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Figures from the paper

Cite This Study

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

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Figures from the paper

Cite This Study

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