Bibliographic Details
| Title: |
Development and validation of prediction model for fall accidents among chronic kidney disease in the community |
| Authors: |
Pinli Lin, Guang Lin, Biyu Wan, Jintao Zhong, Mengya Wang, Fang Tang, Lingzhen Wang, Yuling Ye, Lu Peng, Xusheng Liu, Lili Deng |
| Source: |
Frontiers in Public Health, Vol 12 (2024) |
| Publisher Information: |
Frontiers Media S.A., 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Public aspects of medicine |
| Subject Terms: |
falls, chronic kidney disease, CHARLS, predictive model, nomogram, Public aspects of medicine, RA1-1270 |
| More Details: |
BackgroundThe 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.MethodsParticipants 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.ResultA 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.ConclusionIn this study, we constructed a predictive model to assess the risk of falls among individuals with CKD in the community, demonstrating good predictive capability. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2296-2565 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fpubh.2024.1381754/full; https://doaj.org/toc/2296-2565 |
| DOI: |
10.3389/fpubh.2024.1381754 |
| Access URL: |
https://doaj.org/article/02f0369c316348f5a8ea453e1d1f2e8a |
| Accession Number: |
edsdoj.02f0369c316348f5a8ea453e1d1f2e8a |
| Database: |
Directory of Open Access Journals |
