Development and validation of nomograms to predict frailty-worsening trajectories among Chinese older adults

Frailty progression may lead to adverse clinical events. Timely intervention of individual with heterogeneous frailty trajectories are important to prevent or reverse frailty progression.

This study aimed to develop nomograms to predict heterogeneous frailty progression, and validate their predictive performance.

4,406 participants (2,268 in the development cohort and 2,138 in the validation cohort) were included in this study. Latent class trajectory model (LCTM) was used to identify the heterogeneous frailty trajectories. Lasso regression analysis was employed to screen predictive factors. The nomogram models were subsequently developed using multivariable logistic regression analysis. Model performance was internally validated with bootstrap resampling and externally validated using independent data. The discrimination and calibration were assessed by C-index and calibration curve, respectively.

Two prediction nomograms were developed and validated to estimate the risk of future frailty progression based on three identified frailty trajectories. Eleven predictors were determined in the medium-stable nomogram. The internal and external validation C-indices were 0.86 and 0.77; the calibration curves demonstrated that the predicted probabilities fit well with the actual observation. Six predictors were determined in the low-rapid nomogram. The internal and external validation C-indices were 0.74 and 0.62, respectively, and calibration curves indicated good calibration.

Frailty trajectories provide more predictive value than frailty states. This study developed nomogram models to predict frailty progression, identifying key predictors such as gender, cognitive impairment, lifestyle factors, and early life experiences, with promising validation results.

The nomograms demonstrated favorable performance and may help making public health strategies for more precise frailty management.