Development and validation of a risk prediction nomogram for frailty in older Chinese adults

Frailty is emerging as a determinant of adverse health outcomes in older adults; identifying high-risk groups early and taking effective interventions can improve the quality of life and prognosis for the elderly. The aim of this study was to build and test a model to predict frailty among the older Chinese person, facilitating early intervention.

This cross-sectional study used data from the Psychology and Behavior Investigation of Chinese Residents (PBICR), the set was split into a training set and a validation set at a 70:30 ratio. Logistic regression analyzed frailty factors, and a nomogram was developed to predict frailty, with calibration curves and decision curve analysis (DCA) assessing accuracy.

4,367 people above 60 years of age from the PBICR database in 2022 were included in the final analysis. A total of 1,190 exhibited frailty symptoms. Multivariate logistic regression analysis showed that age, BMI, history of alcohol consumption, depression, social support, family type, household location, cataract, debt, and neighborhood were predictors of frailty in older adults. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.746 and 0.711, respectively. Hosmer-Lemeshow test values were P = 0.212and P = 0.319. The calibration curves showed significant agreement between the nomogram model and actual observations. DCA indicated that the nomogram had a good predictive performance.

The nomogram is a promising tool for assessing frailty risk in the older Chinese person, potentially aiding in personalized diagnosis and intervention strategies in clinical settings.