Intervertebral spinal fusion surgery is the most commonly used surgical procedure for the treatment of degenerative diseases of the lumbar spine1. An excellent outcome in the short-term period after surgery can be achieved for more than 90% of patients2. However, the results of a long-term follow-up study revealed that some patients developed degeneration of segments adjacent to the fused segment3. Lumbar interbody fusion accelerates degenerative changes in adjacent segments, which is known as adjacent segment degeneration (ASD). According to the literature, the incidence of ASD diagnosed based on medical imaging examinations ranges from 36% to 84% five years after fusion surgery4, which could lead to symptoms such as radiating pain or intermittent claudication and possibly even the need for revision surgery. The mechanism of ASD remains unknown, but most researchers believe that biomechanical factors play an important role. Some have attributed ASD to increased range of motion (ROM) of the adjacent segments after surgery5,6, some have attributed it to increased intradiscal pressure in the adjacent segments7,8,9, and others have attributed it to increased stress in the facet joints of the adjacent segments10.
Among the various methods used to study spinal biomechanics, finite element (FE) modeling is widely used because it is noninvasive, inexpensive, and reproducible. Some researchers11,12,13 have established a 3D FE model of the whole lumbar spine (L1-L5) with data extracted from preoperative computed tomography (CT) scans, which made it possible to explore various aspects of spinal biomechanics, ranging from the response of the spine to different loading conditions14,15 to the effects of different pathologies16 and the effects of relevant treatment modalities and techniques17. Although the above modeling method could provide output regarding the patient-specific geometry of the spine with a complex interface and a wealth of information otherwise unattainable from in vivo experiments, its clinical use has remained limited due to the time-consuming nature of the process, rendering the method available only for models based on one or a few subjects14. To address this problem, Nikkhoo et al.18 established a simplified L1-S1 lumbosacral model in which the geometry of the spine is controlled by parameters extracted from patients' preoperative image data, allowing patient-specific models to be automatically generated or updated according to the input parameters. The FE model based on this modeling method has been proven to have good validity. However, there were significant differences in the intradiscal pressure, mean stresses in the facet joints, and mean stresses in the annulus fibrosus compared with the previous CT-based reconstructed model. Another simplified spinal model was applied in a study by Ghezelbash et al.19, but this model differed greatly from the real geometry of the lumbar spine due to the cylindrical shape of the vertebrae and lack of structure regarding the posterior elements.
Therefore, in this study, we developed a geometrically parametric patient-specific FE model to achieve a more efficient modeling and analysis process with good validity. Then, we performed a mechanical analysis of adjacent segments after fusion surgery to elucidate the mechanism and provide theoretical evidence for the early prevention of ASD.