Sex Differences in the Spine

Sex-Specific Influence of Preoperative Musculoskeletal Characteristics on Postoperative Outcomes in Lumbar Spinal Surgery: A Prospective Cohort Study

OBJECTIVE

Lumbar spinal stenosis often necessitates surgical intervention, with preoperative musculoskeletal health significantly influencing postoperative outcomes. This prospective cohort study investigates the sex-specific impact of preoperative musculoskeletal characteristics on postoperative leg pain in elderly patients undergoing lumbar surgery.

METHODS

This study is a secondary analysis of data from the patient-reported outcome measure stratification after surgery for lumbar spinal stenosis with sarcopenia : A prospective cohort study of surgically treated spinal stenosis (STRATEGY-SS) cohort, which recruited lumbar spinal stenosis patients from a tertiary care center between March 2019 and February 2021. Preoperative assessments included evaluations for sarcopenia, muscle mass, grip strength, and physical performance. Patient-reported outcomes for back and leg pain were assessed preoperatively and 1 year postoperatively. Statistical analyses included t-tests, χ2 tests, receiver operating characteristic analysis, and linear regression path analysis.

RESULTS

Significant sex differences were observed in preoperative characteristics. In male patients, no preoperative variables significantly predicted postoperative pain severity. However, in female patients, arm and calf circumferences were significantly associated with 1-year postoperative leg pain. Receiver operating characteristic analysis identified several preoperative predictors of postoperative leg pain in females, with arm circumference showing notable predictive power. Linear regression suggested that smaller arm circumference indirectly contributed to more severe postoperative leg pain through longer hospital stays.

CONCLUSIONS

These findings highlight the need for sex-specific preoperative assessments to improve surgical outcomes, suggesting that targeted preoperative interventions to enhance musculoskeletal health could benefit female patients undergoing lumbar surgery.

Variability of intervertebral joint stiffness between specimens and spine levels

Introduction: Musculoskeletal multibody models of the spine can be used to investigate the biomechanical behaviour of the spine. In this context, a correct characterisation of the passive mechanical properties of the intervertebral joint is crucial. The intervertebral joint stiffness, in particular, is typically derived from the literature, and the differences between individuals and spine levels are often disregarded. Methods: This study tested if an optimisation method of personalising the intervertebral joint stiffnesses was able to capture expected stiffness variation between specimens and between spine levels and if the variation between spine levels could be accurately captured using a generic scaling ratio. Multibody models of six T12 to sacrum spine specimens were created from computed tomography data. For each specimen, two models were created: one with uniform stiffnesses across spine levels, and one accounting for level dependency. Three loading conditions were simulated. The initial stiffness values were optimised to minimize the kinematic error. Results: There was a range of optimised stiffnesses across the specimens and the models with level dependent stiffnesses were less accurate than the models without. Using an optimised stiffness substantially reduced prediction errors. Discussion: The optimisation captured the expected variation between specimens, and the prediction errors demonstrated the importance of accounting for level dependency. The inaccuracy of the predicted kinematics for the level-dependent models indicated that a generic scaling ratio is not a suitable method to account for the level dependency. The variation in the optimised stiffnesses for the different loading conditions indicates personalised stiffnesses should also be considered load-specific.

Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Trunk and shoulder kinematics of rowing displayed by Olympic athletes

The purpose of this study was to assess the effects of stroke rate and sex on trunk and shoulder kinematics of Olympic athletes during rowing on an ergometer. Fifty-eight participants (31 females and 27 males) from the Chinese National Rowing Team were recruited. Trunk (i.e., the pelvis, lumbar and thoracic spine) and shoulder kinematics were measured using an inertial measurement unit system for three stroke rates (18, 26, and 32 strokes/min). Range of motion and angles at the catch and finish were assessed using mixed model ANOVA and correlation analyses with rowing power. Range of motion increased significantly at higher rates for both female and male athletes. This may be a strategy used by athletes when dealing with higher demand for power during training, because a greater range of motion with a longer stroke length could reduce the demand for force generation and possibly delay fatigue. Female rowers exhibited greater range of motion in the lumbar spine, thorax and shoulders than males due to more extended positions at the finish. The sex-related kinematic differences may be attributed to differences in body size, muscle strength and endurance. Practitioners are recommended to consider these factors when developing rowing techniques and providing training suggestions.

The COVID-19 Pandemic and Elective Spine Surgery-A Single Center Experience

Background and objective: The COVID-19 pandemic had a profound impact on medical practice worldwide. In this study, we aimed to investigate the trends of elective spine surgery in our department before and during the pandemic. Materials and methods: Total number of spine procedures due to disc herniation (DH) or spinal stenosis (SS) was collected during 2019-2021 in the Department of Neurosurgery, University Hospital Center Osijek, Croatia. In order to elucidate potential risk factors in the post-pandemic period, demographic data were collected for patients who underwent surgery during 2021. Results: In 2020, there was a 22.1% decrease in the number of surgeries compared to 2019 (205 vs. 263), but during 2021 we observed an increase of 36.1% compared to 2020 (279 vs. 205). The mean age of patients in 2021 was 53.14 years (53.14 ± 13.05) with body mass index of 28.31 kg/m2 (28.31 ± 4.89). There were 179 overweight patients (74%) and 103 smokers (42.6%). Although male and female patients were equally represented (121 each), there was a significant interaction of weight class and sex (p = 0.013). Patients younger than 65 were more likely to undergo surgery due to DH (p < 0.001), whereas older patients were more likely to suffer from SS (p < 0.001). Conclusions: The volume of elective spine surgeries decreased in the first year of the pandemic and increased the following year. Our results suggest that public health policies in the early pandemic period reduced elective surgical procedures, which was followed by a compensatory increase in the following period.

Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union

Instrumented mouthguards have been used to detect head accelerations and record kinematic data in numerous sports. Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious events. However, classification algorithms must be designed and/or validated for each combination of sport, sex and mouthguard system. This study provides the first algorithm to classify head acceleration data from exclusively female rugby union players. Mouthguards instrumented with kinematic sensors were given to 25 participants for six competitive rugby union matches in an inter-university league. Across all instrumented players, 214 impacts were recorded from 460 match-minutes. Matches were video recorded to enable retrospective labelling of genuine and spurious events. Four machine learning algorithms were trained on five matches to predict these labels, then tested on the sixth match. Of the four classifiers, the support vector machine achieved the best results, with area under the receiver operator curve (AUROC) and area under the precision recall curve (AUPRC) scores of 0.92 and 0.85 respectively, on the test data. These findings represent an important development for head impact telemetry in female sport, contributing to the safer participation and improving the reliability of head impact data collection within female contact sport.

Sex Differences in Anthropometric and Physiological Profiles of Hungarian Rowers of Different Ages

The aim of this study was to determine sexual differentiation in the anthropometric and physiological characteristics of Hungarian rowers in different age categories. These characteristics were measured for 15–16-year-old juniors (55 men and 36 women), 17–18-year-old older juniors (52 men and 26 women), and 19–22-year-old seniors (23 men and 8 women). The degree of sexual dimorphism was expressed in units of measurement as percentages and the dimorphism index. In all age categories, females had significantly higher body fat indices. Body fat percentage was determined by electrical impedance and by the Pařízková formula, BMI, and skinfold thicknesses. Males had significantly higher body mass, body height, skeletal muscle mass, sitting height, arm span, lower limb length, and body surface area. Males also scored significantly higher values for the following physiological characteristics: peak power, relative peak power, ErVO_2max, jump height, speed max, force max, and relative maximal power. Analysis of anthropometric and physiological characteristics in Hungarian rowers revealed that sexual dimorphism tended to increase with age, regardless of whether it was expressed in units of measurement, percentages, or dimorphism index values. The age-related increase in the sexual dimorphism of Hungarian rowers suggests that training methods should be carefully selected to accommodate the needs of various age and gender groups.

Sex differences in neck strength and head impact kinematics in university rugby union players

Globally, over three million women participate in rugby union, yet injury prevention and training strategies are predominantly based on androcentric data. These strategies may have limited generalisability to females, given the cervical spine is more susceptible to whiplash and less adept at resisting inertial loading. A total of 53 university rugby union players (25 female, 28 male, 20.7 ± 1.8 years) had their isometric neck strength measured. Bespoke instrumented mouthguards were used to record the magnitude of head impact events in six female and seven male competitive matches. Mean female maximal isometric neck strength was 47% lower than male. Independent samples Mann-Whitney U tests showed no significant differences for peak linear head acceleration (female: median 11.7 g, IQR 7.9 g; male: median 12.5 g, IQR 7.0 g p=.23) or peak rotational head acceleration (female: median 800.2 rad·s^-2, IQR 677.7 rad·s^-2; male: median 849.4 rad·s^-2, IQR 479.8 rad·s^-2; p=.76), despite the mean male body mass being 24% greater than female. Coded video analysis revealed substantial differences in head-impact mechanisms; uncontrolled whiplash dominated >50% of all recorded female impact events and <0.5% in males. Direct head-to-ground impacts comprised 26.1% of female and 9.7% of male impacts, with whiplash occurring in 78.0% and 0.5%, respectively. Overall, the data provided in this study do not support the generalisation of male-derived training and injury-prevention data to female rugby athletes. These results suggest a considerable research effort is required to identify specific weakness of female rugby players and derive appropriate training, injury prevention and return to play protocols.

General method for automated feature extraction and selection and its application for gender classification and biomechanical knowledge discovery of sex differences in spinal posture during stance and gait

Modern technologies enable to capture multiple biomechanical parameters often resulting in relational data. The current work proposes a generally applicable method comprising automated feature extraction, ensemble feature selection and classification to best capture the potentials of the data also for generating new biomechanical knowledge. Its benefits are demonstrated in the concrete biomechanically and medically relevant use case of gender classification based on spinal data for stance and gait. Very good results for accuracy were obtained using gait data. Dynamic movements of the lumbar spine in sagittal and frontal plane and of the pelvis in frontal plane best map gender differences.