Spinal sagittal alignment goals based on statistical modelling and musculoskeletal simulations

Comprehensive assessment of global spinal sagittal alignment and related normal spinal loads in a healthy population

Abnormal postoperative global sagittal alignment (GSA) is associated with an increased risk of mechanical complications after spinal surgery. Typical assessment of sagittal alignment relies on a few selected measures, disregarding global complexity and variability of the sagittal curvature. The normative range of spinal loads associated with GSA has not yet been considered in clinical evaluation. The study objectives were to develop a new GSA assessment method that holistically describes the inherent relationships within GSA and to estimate the related spinal loads. Vertebral endplates were annotated on radiographs of 85 non-pathological subjects. A Principal Component Analysis (PCA) was performed to derive a Statistical Shape Model (SSM). Associations between identified GSA variability modes and conventional alignment measures were assessed. Simulations of respective Shape Modes (SMs) were performed using an established musculoskeletal AnyBody model to estimate normal variation in cervico-thoraco-lumbar loads. The first six principal components explained 97.96% of GSA variance. The SSM provides the normative range of GSA and a visual representation of the main variability modes. Normal variation relative to the population mean in identified alignment features was found to influence spinal loads, e.g. the lower bound of the second shape mode (SM2-2σ) corresponds to an increase in L4L5-compression by 378.64 N (67.86%). Six unique alignment features were sufficient to describe GSA almost entirely, demonstrating the value of the proposed method for an objective and comprehensive analysis of GSA. The influence of these features on spinal loads provides a normative biomechanical reference, eventually guiding surgical planning of deformity correction in the future.

Muscle Strength Identification Based on Isokinetic Testing and Spine Musculoskeletal Modeling

Subject-specific spinal musculoskeletal modeling can help understand the spinal loading mechanism during human locomotion. However, existing literature lacks methods to identify the maximum isometric strength of individual spinal muscles. In this study, a muscle strength identification method combining isokinetic testing and musculoskeletal simulations was proposed, and the influence of muscle synergy and intra-abdominal pressure (IAP) on identified spinal muscle strength was further discussed. A multibody dynamic model of the spinal musculoskeletal system was established and controlled by a feedback controller. Muscle strength parameters were adjusted based on the measured isokinetic moments, and muscle synergy vectors and the IAP piston model were further introduced. The results of five healthy subjects showed that the proposed method successfully identified the subject-specific spinal flexor/extensor strength. Considering the synergistic activations of antagonist muscles improved the correlation between the simulated and measured spinal moments, and the introduction of IAP slightly increased the identified spinal extensor strength. The established method is beneficial for understanding spinal loading distributions for athletes and patients with sarcopenia.

Effect of personalized spinal profile on its biomechanical response in an EMG-assisted optimization musculoskeletal model of the trunk

Recent developments in musculoskeletal (MS) modeling have been geared towards model customization. Personalization of the spine profile could affect estimates of spinal loading and stability, particularly in the upright standing posture where large inter-subject variations in the lumbar lordosis have been reported. This study investigates the biomechanical consequences of changes in the spinal profile. In 31 participants (healthy and with back pain), (1) the spine external profile was measured, (2) submaximal contractions were recorded in a dynamometer to calibrate the EMG-driven MS model and finally (3) static lifting in the upright standing challenging spine stability while altering load position and magnitude were considered. EMG signals of 12 trunk muscles and angular kinematics of 17 segments were recorded. For each participant, the MS model was constructed using either a generic or a personalized spinal profile and 17 biomechanical outcomes were computed, including individual muscle forces, ratios of muscle group forces, spinal loading and stability parameters. According to the ANOVA results and corresponding effect sizes, personalizing the spine profile induced medium and large effects on about half MS model outcomes related to the trunk muscle forces and negligible to small effects on spinal loading and stability as more aggregate outcomes. These effects are explained by personalized spine profiles that were a little more in extension as well as more pronounced spine curvatures (lordosis and kyphosis). These findings suggest that spine profile personalization should be considered in MS spine modeling as it may impact muscle force prediction and spinal loading.

Subject-Specific Alignment and Mass Distribution in Musculoskeletal Models of the Lumbar Spine

Musculoskeletal modeling is a well-established method in spine biomechanics and generally employed for investigations concerning both the healthy and the pathological spine. It commonly involves inverse kinematics and optimization of muscle activity and provides detailed insight into joint loading. The aim of the present work was to develop and validate a procedure for the automatized generation of semi-subject-specific multi-rigid body models with an articulated lumbar spine. Individualization of the models was achieved with a novel approach incorporating information from annotated EOS images. The size and alignment of bony structures, as well as specific body weight distribution along the spine segments, were accurately reproduced in the 3D models. To ensure the pipeline’s robustness, models based on 145 EOS images of subjects with various weight distributions and spinopelvic parameters were generated. For validation, we performed kinematics-dependent and segment-dependent comparisons of the average joint loads obtained for our cohort with the outcome of various published in vivo and in situ studies. Overall, our results agreed well with literature data. The here described method is a promising tool for studying a variety of clinical questions, ranging from the evaluation of the effects of alignment variation on joint loading to the assessment of possible pathomechanisms involved in adjacent segment disease.

Patient-specific statistical shape modeling for optimal spinal sagittal alignment in lumbar spinal fusion

PURPOSE

The present study compared patients developing ASD after L4/5 spinal fusion with a control group using a patient-specific statistical shape model (SSM) to find alignment-differences between the groups.

METHODS

This study included patients who had undergone spinal fusion at L4/5 and either remained asymptomatic (control group; n = 25, follow-up of > 4 years) or required revision surgery for epifusional ASD (n = 22). Landmarks on preoperative and postoperative lateral radiographs were annotated, and the optimal spinal sagittal alignment was calculated for each patient. The two-dimensional distance from the SSM-calculated optimum to the actual positions before and after fusion surgery was compared.

RESULTS

Postoperatively, the additive mean distance from the SSM-calculated optimum was 86.8 mm in the ASD group and 67.7 mm in the control group (p = 0.119). Greater differences were observed between the groups with a larger distance to the ideal in patients with ASD at more cranial levels. Significant difference between the groups was seen postoperatively in the vertical distance of the operated segment L4. The patients with ASD (5.69 ± 3.0 mm) had a significant greater distance from the SSM as the control group (3.58 ± 3.5 mm, p = 0.034).

CONCLUSION

Patients with ASD requiring revision after lumbar spinal fusion have greater differences from the optimal spinal sagittal alignment as an asymptomatic control group calculated by patient-specific statistical shape modeling. Further research might help to understand the value of SSM, in conjunction with already established indexes, for preoperative planning with the aim of reducing the risk of ASD.

LEVEL OF EVIDENCE I

Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.

Computational Challenges in Tissue Engineering for the Spine

This paper deals with a brief review of the recent developments in computational modelling applied to innovative treatments of spine diseases. Additionally, it provides a perspective on the research directions expected for the forthcoming years. The spine is composed of distinct and complex tissues that require specific modelling approaches. With the advent of additive manufacturing and increasing computational power, patient-specific treatments have moved from being a research trend to a reality in clinical practice, but there are many issues to be addressed before such approaches become universal. Here, it is identified that the major setback resides in validation of these computational techniques prior to approval by regulatory agencies. Nevertheless, there are very promising indicators in terms of optimised scaffold modelling for both disc arthroplasty and vertebroplasty, powered by a decisive contribution from imaging methods.