Papers

Apr, 2010

World Health Organization fracture risk assessment tool in the assessment of fractures after falls in hospital

BMC HEALTH SERVICES RESEARCH
  • Shin-ichi Toyabe

Volume
10
Number
First page
106
Last page
106
Language
English
Publishing type
Research paper (scientific journal)
DOI
10.1186/1472-6963-10-106
Publisher
BIOMED CENTRAL LTD

Background: Falls are very common accidents in a hospital. Various risk factors and risk assessment tools are used to predict falls. However, outcomes of falls such as bone fractures have not been considered in these risk assessment tools, and the performance of risk assessment tools in a Japanese hospital setting is not clear.
Methods: This was a retrospective single-institution study of 20,320 inpatients aged from 40 to 90 years who were admitted to a tertiary-care university hospital during the period from April 2006 to March 2009. Possible risk factors for falls and fractures including STRATIFY score and FRAX (TM) score and information on falls and their outcome were obtained from the hospital information system. The datasets were divided randomly into a development dataset and a test dataset. The chi-square test, logistic regression analysis and survival analysis were used to identify risk factors for falls and fractures after falls.
Results: Fallers accounted for 3.1% of the patients in the development dataset and 3.5% of the patients in the test dataset, and 2.6% and 2.9% of the fallers in those datasets suffered peripheral fractures. Sensitivity and specificity of the STRATIFY score to predict falls were not optimal. Most of the known risk factors for falls had no power to predict fractures after falls. Multiple logistic analysis and multivariate Cox's regression analysis with time-dependent covariates revealed that FRAX (TM) score was significantly associated with fractures after falls.
Conclusions: Risk assessment tools for falls are not appropriate for predicting fractures after falls. FRAX (TM) might be a useful tool for that purpose. The performance of STRATIFY to predict falls in a Japanese hospital setting was similar to that in previous studies.

Link information
DOI
https://doi.org/10.1186/1472-6963-10-106
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/20423520
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000277997400002&DestApp=WOS_CPL
ID information
  • DOI : 10.1186/1472-6963-10-106
  • ISSN : 1472-6963
  • Pubmed ID : 20423520
  • Web of Science ID : WOS:000277997400002

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