Development and internal validation of a nomogram for identifying intrinsic capacity impairment among community-dwelling older adults in China: a cross-sectional study

Intrinsic capacity (IC), reflecting an individual’s composite physical and mental capacities, is a key indicator of healthy ageing. Early identification of IC impairment among community-dwelling older adults may help support timely assessment and management in community settings. This study aimed to develop and internally validate a nomogram for identifying IC impairment among community-dwelling older adults. This cross-sectional study included community-dwelling adults aged ≥ 60 years. IC impairment was operationally defined as impairment in at least one domain of the World Health Organization Integrated Care for Older People (WHO-ICOPE) framework. Candidate variables included demographic characteristics, lifestyle factors, fall history, chronic disease burden, medication use, handgrip strength, and appendicular skeletal muscle mass index (ASMI). Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used for variable selection, and retained variables were entered into a multivariable logistic regression model to construct a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, Brier score, and decision curve analysis (DCA). The prevalence of IC impairment was 72.7%. At the more parsimonious λ.1se, age, history of falls, and appendicular skeletal muscle mass index (ASMI) were retained and used to develop the final model. The nomogram showed good discrimination, with AUCs of 0.8209 (95% CI: 0.7916–0.8502) in the training set and 0.7902 (95% CI: 0.7430–0.8373) in the validation set. Calibration was acceptable, and DCA suggested potential net benefit across a relatively wide range of threshold probabilities. A nomogram incorporating age, history of falls, and ASMI was developed and internally validated for identifying IC impairment among community-dwelling older adults. This model may serve as a useful reference for early community screening and stratified assessment. Further external validation is needed.

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Journal
BMC Geriatrics
Published
2026-03-29
DOI
https://doi.org/10.1186/s12877-026-07384-z
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
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article

Development and internal validation of a nomogram for identifying intrinsic capacity impairment among community-dwelling older adults in China: a cross-sectional study

Kemeng Zhang, Ping He, Sui Huang, Jing Ge et al.
BMC Geriatrics
Balance, Gait, and Falls Prevention
article

Development and internal validation of a nomogram for identifying intrinsic capacity impairment among community-dwelling older adults in China: a cross-sectional study

Kemeng Zhang, Ping He, Sui Huang, Jing Ge, Aihong Liu, Bijin Luo, Yi Zhang, Mengchen Liu, Yuxia Shi, Wenhan Li
article en

Abstract

Intrinsic capacity (IC), reflecting an individual’s composite physical and mental capacities, is a key indicator of healthy ageing. Early identification of IC impairment among community-dwelling older adults may help support timely assessment and management in community settings. This study aimed to develop and internally validate a nomogram for identifying IC impairment among community-dwelling older adults. This cross-sectional study included community-dwelling adults aged ≥ 60 years. IC impairment was operationally defined as impairment in at least one domain of the World Health Organization Integrated Care for Older People (WHO-ICOPE) framework. Candidate variables included demographic characteristics, lifestyle factors, fall history, chronic disease burden, medication use, handgrip strength, and appendicular skeletal muscle mass index (ASMI). Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used for variable selection, and retained variables were entered into a multivariable logistic regression model to construct a nomogram. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, Brier score, and decision curve analysis (DCA). The prevalence of IC impairment was 72.7%. At the more parsimonious λ.1se, age, history of falls, and appendicular skeletal muscle mass index (ASMI) were retained and used to develop the final model. The nomogram showed good discrimination, with AUCs of 0.8209 (95% CI: 0.7916–0.8502) in the training set and 0.7902 (95% CI: 0.7430–0.8373) in the validation set. Calibration was acceptable, and DCA suggested potential net benefit across a relatively wide range of threshold probabilities. A nomogram incorporating age, history of falls, and ASMI was developed and internally validated for identifying IC impairment among community-dwelling older adults. This model may serve as a useful reference for early community screening and stratified assessment. Further external validation is needed.

BMC Geriatrics
Union Hospital (HK), Huazhong University of Science and Technology (CN)
Reduced inequalities
Openalex Percentile: Top 2%
Balance, Gait, and Falls Prevention
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