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To explore influencing factors of frailty in community-dwelling elderly individuals with chronic diseases, and to construct a risk prediction model for frailty with internal validation.
A cross-sectional design.
A total of 684 questionnaires were distributed, and 678 valid questionnaires were collected, with an effective response rate of 99.2% by convenience sampling from March to October 2024 in Changsha, Hunan Province, China. General information questionnaire, Tilburg Frailty indicator scale (TFI), General Self-efficacy Scale (GSES), and Physical Resilience Instrument for Older Adults (PRIFOR) were used to collect data. A total of 678 valid samples (frailty rate 24.0%) were randomly divided into training set (n = 474) and validation set (n = 204) at a ratio of 7:3. Baseline characteristics between the two sets were compared, and binary logistic regression analysis was used to determine the influencing factors using IBM SPSS v26.0. The LASSO regression, nomogram, area under the receiver operating characteristic curve (AUC), calibration curve, decision curve, and SHapley Additive exPlanations plots of prediction model were established using R version 4.5.2.
Variables in the model included religion, living arrangement, regular exercise, duration of chronic diseases, number of medications, health status, general self-efficacy, and physical resilience. The AUC, calibration, and decision-making ability in both sets were satisfactory.
The frailty prediction model demonstrated good discrimination, calibration, and clinical utility, providing a scientific basis for the prevention and early screening of frailty in elderly individuals with chronic diseases.
The model can help community healthcare professionals to calculate the risk probability of frailty in elderly individuals with chronic diseases, formulate personalized preventive care measures for high-risk groups as soon as possible to achieve early prevention or delay of frailty and its related complications, and improve the prognosis.
No patient or public contribution.
TRIPOD-AI checklist.
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