title: Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Hospices Civils de Lyon, Pôle Information Médicale Evaluation Recherche, Lyon, France; Université de Lyon, Université Lyon 1, Lyon, France; INSERM U1033, Lyon, France.
authors.forename: Pascal
authors.initials: P
authors.lastname: Caillet
authors.affiliation: LIRIS UMR 5205 CNRS, Data Mining & Machine Learning (DM2L) Team, Université Claude Bernard Lyon 1, Bâtiment Nautibus, Villeurbanne, France.
authors.forename: Sarah
authors.initials: S
authors.lastname: Klemm
authors.affiliation: Hospices Civils de Lyon, Groupement Hospitalier de Gériatrie, Francheville, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Ducher
authors.affiliation: LIRIS UMR 5205 CNRS, Data Mining & Machine Learning (DM2L) Team, Université Claude Bernard Lyon 1, Bâtiment Nautibus, Villeurbanne, France.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Aussem
authors.affiliation: Hospices Civils de Lyon, Pôle Information Médicale Evaluation Recherche, Lyon, France; Université de Lyon, Université Lyon 1, Lyon, France; INSERM U1033, Lyon, France.
authors.forename: Anne-Marie
authors.initials: AM
authors.lastname: Schott
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D015519:Bone Density; D015331:Cohort Studies; D005260:Female; D005602:France / Q000453:epidemiology; D005684:Gait; D006620:Hip Fractures / Q000453:epidemiology / Q000209:etiology* / Q000517:prevention & control; D006801:Humans; D016015:Logistic Models; D011619:Psychotropic Drugs / Q000009:adverse effects; D012307:Risk Factors
chemical_list: D011619:Psychotropic Drugs
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