The combined effects of dynamization time and degree on bone healing

Abstract

Dynamization, increasing the interfragmentary movement (IFM) by reducing the fixation stiffness from a rigid to a more flexible condition, is widely used clinically to promote fracture healing. However, it remains unknown how dynamization degree (relative change in fixation stiffness/IFM from a rigid to a flexible fixation) affects bone healing at various stages. To address this issue, we used a fuzzy logic-based mechano-regulated tissue differentiation algorithm on published experimental data from a sheep osteotomy healing model. We applied a varied degree of dynamization, from 0 (fully rigid fixation) to 0.9 (90% reduction in stiffness relative to the rigid fixation) after 1, 2, 3, and 4 weeks of osteotomy (R1wF, R2wF, R3wF, and R4wF) and computationally evaluated bone regeneration and biomechanical integrity over the healing process of 8 weeks. Compared with the constant rigid fixation, early dynamization (R1wF and R2wF) led to delays in bone bridging and biomechanical recovery of the osteotomized bone. However, the effect of early dynamization on healing was dependent of the degree of dynamization. Specifically, a higher dynamization degree (e.g., 0.9 for R1wF) led to a prolonged delay in bone bridging and largely unrecovered bending stiffness (48% relative to the intact bone), whereas a moderate degree of dynamization (e.g., 0.5 or 0.7) significantly enhanced bone formation and biomechanical properties of the osteotomized bone. These results suggest that dynamization degree and timing interactively affect the healing process. A combination of early dynamization with a moderate degree could enhance the ultimate biomechanical recovery of the fractured bone.

Can Optimizing the Mechanical Environment Deliver a Clinically Significant Reduction in Fracture Healing Time?

“…However, the aspects of the temporal distribution of mechanical stimulation have not been frequently studied in silico. Recently, Fu et al [ 52 ] investigated the impact of dynamization during different stages of healing; they demonstrated the potential for influencing healing using different timings for dynamization. Further computational simulations that are validated with in vivo models may help to design and test stimulation protocols that have the potential to decrease fracture-healing time.…”

Enhancing the Efficiency of Distraction Osteogenesis through Rate-Varying Distraction: A Computational Study

“…Each element of the callus was assumed to be a mixture of four tissue types (connective tissue, cartilage, woven bone, and cortical bone). Using the fuzzy logic controller of MATLAB (Fuzzy Logic Toolbox in MATLAB, The MathWorks, Inc., Natick, MA, USA), the process of tissue differentiation is treated as an initial value problem based on two mechanical (dilatational (ε) and distortional (γ) strains in the mechano-regulatory model) and five biological state variables (blood perfusion, cartilage concentration, and bone concentration, as well as blood perfusion and bone concentration in adjacent elements) [30,31]. All seven state variables were used to predict angiogenesis, endochondral ossification, chondrogenesis, cartilage calcification, and tissue disruption in the callus with a linguistic rule-based fuzzy logic.…”

“…According to the rules of tissue differentiation, the fuzzy logic controller judged the input state (seven state variables) of each element in the callus area, and finally output the changes in blood perfusion, cartilage and bone concentration to predict the results of tissue differentiation [30,31]. Following each step of tissue differentiation, the biological state variables of each callus element were updated, and then the material properties of the callus area were updated by using mixture rules according to the current biological state variables [30,31]. primary physiological axial loading generating on metatarsus of the sheep during normal walking [27].…”

“…The rules were based on the mechano-regulatory model proposed by Reina Romo et al [24] (Figure 10), which can take into account the differences between tension-and compression-governed tissue differentiation of DO. According to the rules of tissue differentiation, the fuzzy logic controller judged the input state (seven state variables) of each element in the callus area, and finally output the changes in blood perfusion, cartilage and bone concentration to predict the results of tissue differentiation [30,31]. Following each step of tissue differentiation, the biological state variables of each callus element were updated, and then the material properties of the callus area were updated by using mixture rules according to the current biological state variables [30,31].…”

“…Fuzzy rules described changes in tissue composition and blood perfusion within each finite element. The material properties of the elements were then updated according to the regenerated new tissue composition [30,31] (Figure 10).…”