Bone mineral density and donor age are not predictive of femoral ring allograft bone mechanical strength

J Orthop Res. 2014 Oct;32(10):1271-6. doi: 10.1002/jor.22679. Epub 2014 Jul 8.

Abstract

While metal or plastic interbody spinal fusion devices are manufactured to appropriate mechanical standards, mechanical properties of commercially prepared structural allograft bone remain relatively unassessed. Robust models predicting compressive load to failure of structural allograft bone based on easily measured variables would be useful. Three hundred twenty seven femoral rings from 34 cadaver femora were tested to failure in axial compression. Predictive variables included age, gender, bone mineral density (BMD), position along femoral shaft, maximum/minimum wall thickness, outer/inner diameter, and area. We used support vector regression and 10-fold cross-validation to develop robust nonlinear predictive models for load to failure. Model performance was measured by the root-mean-squared-deviation (RMSD) and correlation coefficients (r). A polynomial model using all variables had RMSD = 7.92, r = 0.84, indicating excellent performance. A model using all variables except BMD was essentially unchanged (RMSD = 8.12, r = 0.83). Eliminating both age and BMD produced a model with RMSD = 8.41, r = 0.82, again essentially unchanged. Compressive strength of structural allograft bone can be estimated using easily measured geometric parameters, without including BMD or age. As DEXA is costly and cumbersome, and setting upper age-limits for potential donors reduces the supply, our results may prove helpful to increase the quality and availability of structural allograft.

Keywords: biomechanics; bone mineral density; spinal fusion; structural bone allograft.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Absorptiometry, Photon
  • Age Factors
  • Allografts
  • Bone Density*
  • Bone Transplantation* / trends
  • Calcification, Physiologic
  • Female
  • Femur / transplantation*
  • Humans
  • Linear Models
  • Male
  • Mechanical Phenomena
  • Predictive Value of Tests