Publications

Patient specific quantitative analysis of fracture fixation in the proximal femur implementing principal strain ratios. Method and experimental validation

Computational patient-specific modeling has the potential to yield powerful information for selection and planning of fracture treatments if it can be developed to yield results that are rapid, focused and coherent from a clinical perspective. In this study we introduce the utilization of a principal strain fixation ratio measure (SR) defined as the ratio of principal strains that develop in a fixated bone relative to the principal strains that develop in the same bone in an intact state. The SR field output variable is theoretically independent of load amplitude and also has a direct clinical interpretation with SR<1−a representing stress shielding and SR>1+b representing overstressed bone. A combined experimental and numerical study was performed with cadaveric proximal femora (n=6) intact and following fracture fixation to quantify the performance of the SR variable in terms of accuracy and sensitivity to uncertainties in density–elasticity relationships and load amplitude as model input variables. For a given axial compressive force the SR field output variable was found to be less sensitive to changes in density–elasticity relationships and the response function to be more accurate than strain values themselves; errors were reduced by 44% on comparing SR with strain in the fixated model. In addition, the experimental data confirmed the assumption that the SR values behave independent of load amplitude. The load independent behavior of SR and its direct clinical interpretation may ultimately provide an appropriate and easily understood comparative computational measure to choose between patient specific fracture fixation alternatives.

Authors: E. Peleg, M. Beek, L. Joskowicz, M. Liebergall, R. Mosheiff, C. Whyne.
Year of publication: 2010
Journal: Journal of Biomechanics, Volume 43, Issue 14, Pages 2684-2688

Link to publication:

Labs:

“Working memory”