A systematic review of fall prediction models for maintenance hemodialysis patients
TAN Yun
TAO Huijun
LIU Ding
XIANG Meng
XIONG Haitong
Abstract：Objective:To systematically evaluate the fall prediction models for maintenance hemodialysis patients,providing a reference for early identification of high-risk patients and timely clinical decision-making.Methods:Relevant literature on fall prediction models for maintenance hemodialysis patients was systematically retrieved from Web of Science,PubMed,EMbase,Cochrane Library,CINAHL,CNKI,WanFang Data,SinoMed,and VIP from the inception of the databases to February 11,2025.Two researchers independently screened the literature,extracted the data,and evaluated the risk of bias and applicability of the included studies according to the risk of bias assessment tool for prediction model studies.Results:A total of 9 studies were included.The majority of the studies were retrospective.The final number of predictors ranged from 2 to 10,with age and gender being the most common.The overall risk of bias of the 9 studies was high,but the applicability was good.Conclusion:The fall prediction models for maintenance hemodialysis patients are still in the early stages of development,with a relatively high risk of bias.In the future,it is necessary to combine clinical practice and adopt multiple methods to construct more accurate prediction models.
Keywords：maintenance hemodialysisfallprediction modelsystematic reviewevidence-based nursing
Publication Date：2025-10-25
Online Publishing Date：2025-11-04(First online date of this platform, not the publication date of the document)
Pages：5( 4130-4134 )