Development and validation of a predictive model for frailty risk in older adults with cardiovascular-metabolic comorbidities

Bibliographic Data

ID22068942
AuthorsLulu Yan (0000-0002-3402-7637, Yangtze University), Entong Ren (Yangtze University), Chenjiao Guo (Guangdong Pharmaceutical University), Yuanyuan Peng (0000-0003-1864-9054, Guangdong Pharmaceutical University), Hao Chen (0000-0002-8873-8266, Guangdong Pharmaceutical University), Weihua Li (0000-0001-9215-4979, Third Affiliated Hospital of Southern Medical University, corresponding author)
Year2025
Volume13
Pages1561845-1561845
Publication date2025-04-22
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1561845
PMID40331117
OpenAlexW4409692224
LanguageEN
Citations received1
References cited56

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 p = 0.073 and p = 0.245 (both > 0.05), with calibration curves indicating strong alignment between the model’s predictions and actual outcomes. The DCA demonstrated the model’s substantial clinical utility. 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

Comorbidity · Intensive care medicine · Psychiatry · Chronic Disease Management Strategies · Frailty in Older Adults · Heart Failure Treatment and Management · Medicine · Gerontology