CORR Insights®: How Does Chondrolabral Damage and Labral Repair Influence the Mechanics of the Hip in the Setting of Cam Morphology? A Finite-Element Modeling Study

Where Are We Now? In the past decade, a number of research programs have evaluated questions about femoroacetabular impingement (FAI), including: What morphological changes in the adult hip are associated with (and might they presage) osteoarthritis? How does FAI affect joint function and mechanics? How should we treat it, and does treatment reduce the risk of later symptomatic arthritis? These questions, among others, have become the obsessions for musculoskeletal researchers and for those who strive to preserve the inherent structural stability of the hip joint [2]. In addition to the cam morphology, a research group I was a part of recently noticed that other anatomical parameters (smaller femoral neck-shaft angle and higher pelvic incidence angle) were associated with symptomatic FAI [11]. Using musculoskeletal modeling, we found what we believe are clinically important differences in hip loading driven by altered gait and muscle usage patterns during activities of daily living [13]. Computational methods play a vital role in answering research questions like those I mentioned [1, 7], but finite-element analyses of the hip are neither simple nor are they straightforward. They’re always limited, at least to some degree, by our assumptions to make the models computationally manageable. Nonetheless, they provide important information that we simply cannot obtain any other way. For example, our finite-element simulations found that symptomatic hips that have less severe cam morphologies actually are at higher risk of acetabular cartilage and subchondral bone-shear stresses than asymptomatic hips, even if the asymptomatic hips have more severe cam morphologies [12]. And, importantly, problems related to joint loading and pelvic mobility do not appear to improve after surgery for FAI—even 2 years later [4]—which suggests that patients’ adaptations to FAI persist even after their cam morphologies have been corrected. In the current study, Todd and colleagues [15] implemented finite-element methods to compare one hip with cam morphology to one control hip that did not have cam morphology. This is an important, timely, and well-executed study. Their computational workflow involved rigorous preprocessing—including models reconstructed from CT arthrography data, material characteristics incorporated for fluid responses, and motion-capture kinematics aligned with Bergmann’s hip loading profiles—to evaluate shear stress, tensile strain, contact pressure, and fluid pressure. They found that cam morphology was associated with elevated cartilage shear stresses and joint degeneration, but also that it effectively distributed the loading more evenly throughout the cartilage. Their simulated labral repairs also showed localized cartilage strains near the chondrolabral junction and, more importantly, revealed an iatrogenic factor that we may need to (re)consider and challenge. Based on these discoveries, surgeons should incorporate the important messages from the computational simulations to understand the various scenarios and risks prior to performing the actual surgeries. Where Do We Need To Go? Surgical management for FAI aims to preserve the natural hip, restore joint function, relieve pain, and delay or slow the onset of symptomatic arthritis if it is not already present. My sense is that surgeons often believe that a well-performed cam osteochondroplasty alleviates joint stresses and that soft tissue repairs are crucial to restore functional stability. It seems to me that patient-centric finite-element methods could, in the future, provide specific guidance on how to perform these procedures more effectively. In fact, there may be important differences in joint kinematics and loading after a labral tear, delamination, chondrolabral degeneration, or other defect, as well as among the treatments for those problems. The hip likely becomes more unstable after each injury stage (without labral seal, induced instability, or increased translations), and this can affect each input variable in the models we create. We need to learn more about how these modeling parameters can help close the gaps in associating patient-specific computational simulations and surgeon-controlled factors at the time of surgery. We also need to consider hip impingement and instability as a multifactorial problem that leads to abnormal hip joint translations. Using physical in vitro methods, my research group recently observed that intact cam hips were prone to impingement during deep hip flexion and flexion-adduction with internal rotation, but also showed larger translations compared to hips without cam morphology [10]. After cam osteochondroplasty, joint loading decreased by 27% and internal rotation increased by 30% in deep flexion positions. However, looking more closely at the hip center of rotation, cam osteochondroplasty disrupted the labral seal and shifted the hip inferolaterally during external rotation. This resulted in large translations during deep hip flexion and increased instability by 31% [9]. Thus, even with an intact labrum and repaired capsule, there was evident iatrogenic instability attributed to separation of the resected femoral contour and intact labrum. Considering that the femoral head is naturally conchoidal and perhaps shaped in a way to maintain the labral seal and effectively distribute load [3], an overresection may cause hip instability, pain, and poorer hip function. In efforts to improve surgical management and balance the need to maintain the hip’s inherent structure and the need to restore stability, the next steps are to define how much correction is too much and how we can improve our computational models to predict those outcomes. How can we improve our modeling methods and computational simulations and get to a point where we can trust them to predict our surgical plans and outcomes? Within our computational framework, we need to include soft tissues into our models to characterize joint injury and identify the best surgical approaches. Future studies are needed to help us better incorporate patient-specific muscle contributions in our finite-element simulations. Furthermore, hip capsular ligaments play a predominant role in protecting against edge loading, whereas the labrum works as a functional stabilizer. As such, we need to expand our understanding and inclusions of soft tissue properties (muscle, capsule, labrum) in our computational modeling and simulation packages and to predict adverse loading leading to acute and chronic injuries. For the next decade, computational modeling and simulation methods will undoubtedly improve with efficiency, processing capacity, and advancements in artificial intelligence. The musculoskeletal and orthopaedic biomechanics communities will need to continue examining the pathomechanisms through various multidisciplinary approaches and biomechanics research methods (in vivo, in silico, in vitro). While it’s advantageous to have so many research tools at our disposal, we still need thoughtful, specific approaches to connect recommendations that arise from modeling studies with robust, relevant clinical research on hip impingement and instability mechanics. For this, we need to define measurable parameters that effectively distinguish normal hips from those with impingement or instability. How Do We Get There? We can get there with patient-centric modeling and simulation initiatives that can incorporate comprehensive soft tissue characteristics and responses. Recently, there have been extensive developments in machine learning and hybrid imaging modalities that combine positron emission tomography with CT and MRI to ascertain functional and metabolic tissue activity [8, 14]. In addition, we may be able to implement ultrasound shear-wave elastography and diffusion imaging sequences that can provide robust information on macro-and microstructural soft tissue properties, architecture, and relationship between fiber orientation and joint stability [5, 6]. Tractography methods can help examine aspects such as pennation angle, fiber length, fiber curvature, and fibrosis to capture the interactions at the tissue level during relevant joint loading activities to examine relationships between individual material properties of tissue anisotropy and contractile directions. A combination of these high-resolution and multidimensional imaging modalities will help us capture more of the soft tissue structures in loaded states and provide us with the information about tissue responses during mechanical stimulus that currently we struggle to identify using in vivo, in silico, and in vitro methods. Ultimately, imaging modalities will help us obtain the necessary upstream input information needed for modeling and simulation, and will also substantiate our results with functional imaging biomarkers and downstream outputs.

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