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Construction and verification of a model for predicting fall risk in patients with maintenance hemodialysis† Cover

Construction and verification of a model for predicting fall risk in patients with maintenance hemodialysis†

Open Access
|Dec 2024

Abstract

Objective

To construct a risk prediction model for fall in patients with maintenance hemodialysis (MHD) and to verify the prediction effect of the model.

Methods

From June 2020 to December 2020, 307 patients who underwent MHD in a tertiary hospital in Chengdu were divided into a fall group (32 cases) and a non-fall group (275 cases). Logistic regression analysis model was used to establish the influencing factors of the subjects. Hosmer–Lemeshow and receiver operating characteristic (ROC) curve were used to test the goodness of fit and predictive effect of the model, and 104 patients were again included in the application research of the model.

Results

The risk factors for fall were history of falls in the past year (OR = 3.951), dialysis-related hypotension (OR = 6.949), time up and go (TUG) test (OR = 4.630), serum albumin (OR = 0.661), frailty (OR = 7.770), and fasting blood glucose (OR = 1.141). Hosmer–Lemeshow test was P = 0.475; the area under the ROC curve was 0.907; the Youden index was 0.642; the sensitivity was 0.843; and the specificity was 0.799.

Conclusions

The risk prediction model constructed in this study has a good effect and can provide references for clinical screening of fall risks in patients with MHD.

DOI: https://doi.org/10.2478/fon-2024-0043 | Journal eISSN: 2544-8994 | Journal ISSN: 2097-5368
Language: English
Page range: 387 - 394
Submitted on: Oct 9, 2023
Accepted on: Aug 4, 2024
Published on: Dec 16, 2024
Published by: Shanxi Medical Periodical Press
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2024 Yue Liu, Yan-Li Zeng, Shan Zhang, Li Meng, Xiao-Hua He, Qing Tang, published by Shanxi Medical Periodical Press
This work is licensed under the Creative Commons Attribution 4.0 License.