Development and validation of prediction model for fall accidents among chronic kidney disease in the community.
Front Public Health
; 12: 1381754, 2024.
Article
em En
| MEDLINE
| ID: mdl-38873317
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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article