Computers in biology and medicine | 2021

Single-level subject-specific finite element model can predict fracture outcomes in three-level spine segments under different loading rates

 
 
 
 
 

Abstract


Osteoporosis-related vertebral compression fracture can occur under normal physiological activities. Bone metastasis is another source of vertebral fracture. Different loading rates, either high-energy traumas such as falls or low-energy traumas under normal physiological activities, can result in different fracture outcomes. The aim of the current study was to develop a quantitative computed tomography-based finite element analysis (QCT/FEA) technique for single vertebral bodies to predict fracture strength of three-level spine segments. Developed QCT/FEA technique was also used to characterize vertebral elastic moduli at two loading rates of 5\xa0mm/min, representing a physiologic loading condition, and 12000\xa0mm/min, representing a high-energy trauma. To this end, a cohort of human spine segments divided into three groups of intact, defect, and augmented were mechanically tested to fracture; then, experimental stiffness and fracture strength values were measured. Outcomes of this study showed no significant difference between the elastic modulus equations at the two testing speeds. Areal bone mineral density measured by dual x-ray absorptiometry (DXA/BMD) explained only 53% variability (R2\xa0=\xa00.53) in fracture strength outcomes. However, QCT/FEA could explain 70% of the variability (R2\xa0=\xa00.70) in experimentally measured fracture strength values. Adding disk degeneration grading, testing speed, and sex to QCT/FEA-estimated fracture strength values further increased the performance of our statistical model by 14% (adjusted R2 of 0.84 between the prediction and experimental fracture forces). In summary, our results indicated that a single-vertebra model, which is computationally less expensive and more time efficient, is capable of estimating fracture outcomes with acceptable performance (range: 70-84%).

Volume 137
Pages \n 104833\n
DOI 10.1016/j.compbiomed.2021.104833
Language English
Journal Computers in biology and medicine

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