An interactive dynamic online nomogram for predicting the heterogeneous trajectories of intrinsic capacity among...

With increasing age, intrinsic capacity (IC) in older adults tends to decline. Currently, no studies have used nomograms to predict high-risk factors for IC decline based on its heterogeneous trajectories.

This study analyzed longitudinal data from 1801 older adults in the China Health and Retirement Longitudinal Study (CHARLS) cohort. K-means clustering identified heterogeneous IC trajectories. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression determined independent predictors, which were used to construct and evaluate static and dynamic online nomograms. Internal validation was performed with 1000 bootstrap resamples. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA).

Two trajectories of IC changes were identified: a high IC impairment trajectory and a low IC impairment trajectory. Multivariate logistic regression analysis identified ten independent predictors for the IC change trajectory, including retirement status, pain, self-rated health status, educational level, life satisfaction, age, health satisfaction, sleep duration, residence and participation in social activities, and a dynamic online nomogram was constructed accordingly. The AUROCs of the nomogram in the training and validation sets were 0.789 and 0.758, respectively, indicating good discriminative ability. Calibration curves demonstrated high consistency between predicted and observed probabilities, and DCA showed excellent clinical utility of the nomogram in both the training and validation sets.

There are heterogeneous trajectories in the IC levels of older adults. The constructed nomogram can serve as a clinical tool to facilitate early identification of high-risk populations and provide strong support for health management in older adults.