Schwarzenberg Peter, Schlatter Jérôme, Ernst Manuela, Windolf Markus, Dailey Hannah L, Varga Peter
AO Research Institute Davos, Davos, Switzerland.
Lehigh University, Bethlehem, Pennsylvania, USA.
J Orthop Res. 2025 Feb;43(2):370-378. doi: 10.1002/jor.26007. Epub 2024 Nov 9.
Bone fracture healing is a complex physiological process influenced by biomechanical and biomolecular factors. Mechanical stability is crucial for successful healing, and disruptions can lead to delayed healing or nonunion. Bone commonly heals itself through secondary fracture healing, which is governed by the mechanical strain at the fracture site. To investigate these phenomena, a validated methodology for capturing the mechanoregulatory process in specimen-specific models of fracture healing could provide insight into the healing process. This study implemented a prognostic healing simulation framework to predict healing trajectories based on mechanical stimuli. Sixteen sheep were subjected to a 3 mm transverse tibial mid-shaft osteotomy, stabilized with a custom plate, and equipped with displacement transducer sensors to measure interfragmentary motion over 8 weeks. Computed tomography scans were used to create specimen-specific bone geometries for finite element analysis. Virtual mechanical testing was performed iteratively to calculate strains in the callus region, which guided tissue differentiation and consequently, healing. The predicted healing outcomes were compared to continuous in vivo sensor data, providing a unique validation data set. Healing times derived from the in vivo sensor and in silico sensor showed no significant differences, suggesting the potential for these predictive models to inform clinical assessments and improve nonunion risk evaluations. This study represents a crucial step towards establishing trustworthy computational models of bone healing and translating these to the preclinical and clinical setting, enhancing our understanding of fracture healing mechanisms. Clinical significance: Prognostic bone fracture healing simulation could assist in non-union diagnosis and prediction.
骨折愈合是一个受生物力学和生物分子因素影响的复杂生理过程。机械稳定性对于成功愈合至关重要，而干扰可能导致愈合延迟或不愈合。骨骼通常通过二期骨折愈合自行修复，这一过程受骨折部位的机械应变控制。为了研究这些现象，一种经过验证的方法可用于在骨折愈合的标本特异性模型中捕捉机械调节过程，从而深入了解愈合过程。本研究实施了一个预后愈合模拟框架，以基于机械刺激预测愈合轨迹。16只绵羊接受了3毫米的胫骨中段横向截骨术，用定制钢板固定，并配备位移传感器，以测量8周内的骨折断端间运动。计算机断层扫描用于创建标本特异性的骨骼几何模型，以进行有限元分析。通过迭代进行虚拟力学测试，计算骨痂区域的应变，从而指导组织分化，进而促进愈合。将预测的愈合结果与连续的体内传感器数据进行比较，提供了一个独特的验证数据集。来自体内传感器和计算机模拟传感器的愈合时间没有显著差异，这表明这些预测模型有可能为临床评估提供信息，并改善不愈合风险评估。本研究代表了朝着建立可靠的骨愈合计算模型并将其转化为临床前和临床环境迈出的关键一步，有助于加深我们对骨折愈合机制的理解。临床意义：预后性骨折愈合模拟有助于不愈合的诊断和预测。
