Structural bone allografts are an established treatment method for long-bone structural defects arising from such conditions as trauma, sarcoma, and osteolysis following total joint replacement. However, the quality of structural bone allografts is difficult to non-destructively assess prior to use. The functional lifetime of structural allografts depend on their ability to resist cyclic loading, which can lead to fracture even at stress levels well below the yield strength. Because allograft bone has limited capacity for remodeling, optimizing allograft selection for bone quality could decrease long-term fracture risk. Raman spectroscopy biomarkers can non-destructively assess the three primary components of bone (collagen, mineral, and water), and may predict the resistance of donor bone allografts to fracture from cyclic loads. The purpose of this study was to prospectively assess the ability of Raman biomarkers to predict number of cycles to fracture (“cyclic fatigue life”) of human allograft cortical bone. Twenty-one cortical bone specimens were from the mid-diaphysis of human donor bone tissue (bilateral femurs from 4 donors: 63M, 61M, 51F, 48F) obtained from the Musculoskeletal Transplant Foundation. Six Raman biomarkers were analyzed: collagen disorganization, type B carbonate substitution (a surrogate for mineral maturation), matrix mineralization, and 3 water compartments. Specimens underwent cyclic fatigue testing under fully reversed conditions at 35 and 45MPa (physiologically relevant stress levels for structural allografts). Specimens were tested to fracture or to 30 million cycles (“run-out”), simulating 15 years of moderate activity (i.e., 6000 steps per day). Multivariate regression analysis was performed using a tobit model (censored linear regression) for prediction of cyclic fatigue life. Specimens were right-censored at 30 million cycles.Background
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