Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.

Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the at...

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Main Authors: Pascal Caillet, Sarah Klemm, Michel Ducher, Alexandre Aussem, Anne-Marie Schott
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4378915?pdf=render
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spelling doaj-62e7e450489241ada59046565a6fd19b2020-11-24T21:58:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01103e012012510.1371/journal.pone.0120125Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.Pascal CailletSarah KlemmMichel DucherAlexandre AussemAnne-Marie SchottHip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach.EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences.Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density.Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.http://europepmc.org/articles/PMC4378915?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Pascal Caillet
Sarah Klemm
Michel Ducher
Alexandre Aussem
Anne-Marie Schott
spellingShingle Pascal Caillet
Sarah Klemm
Michel Ducher
Alexandre Aussem
Anne-Marie Schott
Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
PLoS ONE
author_facet Pascal Caillet
Sarah Klemm
Michel Ducher
Alexandre Aussem
Anne-Marie Schott
author_sort Pascal Caillet
title Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
title_short Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
title_full Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
title_fullStr Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
title_full_unstemmed Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
title_sort hip fracture in the elderly: a re-analysis of the epidos study with causal bayesian networks.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach.EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences.Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density.Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.
url http://europepmc.org/articles/PMC4378915?pdf=render
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