Citation

Lin P, Lin G, Wan B, Zhong J, Wang M, Tang F, Wang L, Ye Y, Peng L, Liu X, Deng L. Front. Public Health 2024; 12: e1381754.

Copyright

(Copyright © 2024, Frontiers Editorial Office)

DOI

10.3389/fpubh.2024.1381754

PMID

38873317

PMCID

PMC11171714

Abstract

BACKGROUND: The population with chronic kidney disease (CKD) has significantly heightened risk of fall accidents. The aim of this study was to develop a validated risk prediction model for fall accidents among CKD in the community.

METHODS: Participants with CKD from the China Health and Retirement Longitudinal Study (CHARLS) were included. The study cohort underwent a random split into a training set and a validation set at a ratio of 70 to 30%. Logistic regression and LASSO regression analyses were applied to screen variables for optimal predictors in the model. A predictive model was then constructed and visually represented in a nomogram. Subsequently, the predictive performance was assessed through ROC curves, calibration curves, and decision curve analysis.

RESULT: A total of 911 participants were included, and the prevalence of fall accidents was 30.0% (242/911). Fall down experience, BMI, mobility, dominant handgrip, and depression were chosen as predictor factors to formulate the predictive model, visually represented in a nomogram. The AUC value of the predictive model was 0.724 (95% CI 0.679-0.769). Calibration curves and DCA indicated that the model exhibited good predictive performance.

CONCLUSION: In this study, we constructed a predictive model to assess the risk of falls among individuals with CKD in the community, demonstrating good predictive capability.

Language: en

Keywords

Humans; Risk Factors; Aged; Female; Logistic Models; Male; Middle Aged; Longitudinal Studies; ROC Curve; chronic kidney disease; nomogram; predictive model; falls; Nomograms; China/epidemiology; *Accidental Falls/statistics & numerical data; *Renal Insufficiency, Chronic/epidemiology; CHARLS; Risk Assessment/methods