Construction and validation of a model for predicting disability in older adults with chronic lung disease.

INTRODUCTION

Chronic lung disease and disability pose a serious threat to the physical and mental health of the older adults, and there are relatively few studies on the prediction of disability in this group.

AIMS: This study was to explore the risk factors for disability in older adults with chronic lung disease and develop a validated risk prediction model.

METHOD: This study involved 845 older adults with chronic lung diseases from the 2015 China Health and Retirement Longitudinal Study (CHARLS) survey. They were split into a training group (n = 592) and a validation set (n = 253) at a 7:3 ratio. Univariate and LASSO multiple logistic regression screened variables affecting disability occurrence. A multivariate logistic regression built the prediction model and created nomograms. A calibration curve assessed the model's accuracy, while the receiver operating characteristic curve's area and decision curve analysis evaluated its prediction performance.

RESULTS

The final model included age, gender, falls, physical pain, depression, health satisfaction, child satisfaction, intellectual disability, SBP, respiratory function.The model demonstrated good discrimination (AUC 76.5%, 95% confidence interval CI 0.70-0.80). The calibration curve closely aligned with the ideal curve, and the Brier score was 0.193. For the validation set, the model demonstrated comparable discrimination (AUC 76%, 95% CI 0.70-0.82), Decision curve analysis demonstrated the high clinical utility of the model.

CONCLUSION

The predictive model of disability in older adults with chronic lung disease constructed on the basis of the CHARLS with logistic regression selection of factors is good and has a certain predictive value for the risk of disability in older adults with chronic lung disease.