Abstract

ObjectivesHip 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.SettingEPIDOS 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.ResultsBoth 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.ConclusionBoth 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.

Highlights

  • Hip fractures commonly result in permanent disability, institutionalization or death, and are one of the most damaging fractures among elderly people [1]

  • 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

  • Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture

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Summary

Introduction

Hip fractures commonly result in permanent disability, institutionalization or death, and are one of the most damaging fractures among elderly people [1]. As the cost of fracture regarding medical expenditures and quality of life lost can be substantial, it is essential to identify a complete profile of fracture risk for the development of timely interventions such as pharmacotherapy to limit bone structure degradation and prevent its clinical translation into hip fracture [2]. This degradation remains often definitive, i.e. it can be stopped but cannot be healed in most of the case [3]. Our objective was to use a causal Bayesian network framework for studying mechanisms leading to hip fracture and our secondary objective was to confirm our results by performing a logistic regression

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