ArticleFrontiers in public health2024
Development and validation of prediction model for fall accidents among chronic kidney disease in the community.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development of a web platform for predicting fall risk in cardiovascular patients using machine learning.Scientific reports · 2026Article
- Predicting fall risk among older adults with sarcopenia in China using machine learning models: a six-year longitudinal study from CHARLS.BMC geriatrics · 2026Article
- Validation of a clinical prediction model for falls in community-dwelling older adults with COPD: A preliminary analysis.Chronic respiratory diseaseArticle
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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