Med Biol Eng Comput - A poisson process model for hip fracture risk.

Tópicos

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Resumo

The primary method for assessing fracture risk in osteoporosis relies primarily on measurement of bone mass. Estimation of fracture risk is most often evaluated using logistic or proportional hazards models. Notwithstanding the success of these models, there is still much uncertainty as to who will or will not suffer a fracture. This has led to a search for other components besides mass that affect bone strength. The purpose of this paper is to introduce a new mechanistic stochastic model that characterizes the risk of hip fracture in an individual. A Poisson process is used to model the occurrence of falls, which are assumed to occur at a rate, lambda. The load induced by a fall is assumed to be a random variable that has a Weibull probability distribution. The combination of falls together with loads leads to a compound Poisson process. By retaining only those occurrences of the compound Poisson process that result in a hip fracture, a thinned Poisson process is defined that itself is a Poisson process. The fall rate is modeled as an affine function of age, and hip strength is modeled as a power law function of bone mineral density (BMD). The risk of hip fracture can then be computed as a function of age and BMD. By extending the analysis to a Bayesian framework, the conditional densities of BMD given a prior fracture and no prior fracture can be computed and shown to be consistent with clinical observations. In addition, the conditional probabilities of fracture given a prior fracture and no prior fracture can also be computed, and also demonstrate results similar to clinical data. The model elucidates the fact that the hip fracture process is inherently random and improvements in hip strength estimation over and above that provided by BMD operate in a highly "noisy" environment and may therefore have little ability to impact clinical practice.

Resumo Limpo

primari method assess fractur risk osteoporosi reli primarili measur bone mass estim fractur risk often evalu use logist proport hazard model notwithstand success model still much uncertainti will will suffer fractur led search compon besid mass affect bone strength purpos paper introduc new mechanist stochast model character risk hip fractur individu poisson process use model occurr fall assum occur rate lambda load induc fall assum random variabl weibul probabl distribut combin fall togeth load lead compound poisson process retain occurr compound poisson process result hip fractur thin poisson process defin poisson process fall rate model affin function age hip strength model power law function bone miner densiti bmd risk hip fractur can comput function age bmd extend analysi bayesian framework condit densiti bmd given prior fractur prior fractur can comput shown consist clinic observ addit condit probabl fractur given prior fractur prior fractur can also comput also demonstr result similar clinic data model elucid fact hip fractur process inher random improv hip strength estim provid bmd oper high noisi environ may therefor littl abil impact clinic practic

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