Cross-sectional area and fat infiltration of the lumbar spine muscles in patients with back disorders: a deep learning-based big data analysis.

PURPOSE: Validated deep learning models represent a valuable option to perform large-scale research studies aiming to evaluate muscle quality and quantity of paravertebral lumbar muscles at the population level. This study aimed to assess lumbar spine muscle cross-sectional area (CSA) and fat infiltration (FI) in a large cohort of subjects with back disorders through a validated deep learning model. METHODS: T2 axial MRI images of 4434 patients (n = 2609 females, n = 1825 males; mean age: 56.7 ± 16.8) with back disorders, such as fracture, spine surgery or herniation, were retrospectively collected from a clinical database and automatically segmented. CSA, expressed as the ratio between total muscle area (TMA) and the vertebral body area (VBA), and FI, in percentages, of psoas major, quadratus lumborum, erector spinae, and multifidus were analyzed as primary outcomes. RESULTS: Male subjects had significantly higher CSA (6.8 ± 1.7 vs. 5.9 ± 1.5 TMA/VBA; p < 0.001) and lower FI (21.9 ± 8.3% vs. 15.0 ± 7.3%; p < 0.001) than females. Multifidus had more FI (27.2 ± 10.6%; p < 0.001) than erector spinae (22.2 ± 9.7%), quadratus lumborum (17.5 ± 7.0%) and psoas (13.7 ± 5.8%) whereas CSA was higher in erector spinae than other lumbar muscles. A high positive correlation between age and total FI was detected (rs = 0.73; p < 0.001) whereas a negligible negative correlation between total CSA and age was observed (rs = - 0.24; p < 0.001). Subjects with fractures had lower CSA and higher FI compared to those with herniations, surgery and with no clear pathological conditions. CONCLUSION: CSA and FI values of paravertebral muscles vary a lot in accordance with subjects' sex, age and clinical conditions. Given also the large inter-muscle differences in CSA and FI, the choice of muscles needs to be considered with attention by spine surgeons or physiotherapists when investigating changes in lumbar muscle morphology in clinical practice.

A decade of experience in over 300 surgically treated spine patients with long-term oral anticoagulation: a propensity score matched cohort study.

PURPOSE: The aim of this study was to investigate the risks and outcomes of patients with long-term oral anticoagulation (OAC) undergoing spine surgery. METHODS: All patients on long-term OAC who underwent spine surgery between 01/2005 and 06/2015 were included. Data were prospectively collected within our in-house Spine Surgery registry and retrospectively supplemented with patient chart and administrative database information. A 1:1 propensity score-matched group of patients without OAC from the same time interval served as control. Primary outcomes were post-operative bleeding, wound complications and thromboembolic events up to 90 days post-surgery. Secondary outcomes included intraoperative blood loss, length of hospital stay, death and 3-month post-operative patient-rated outcomes. RESULTS: In comparison with the control group, patients with OAC (n = 332) had a 3.4-fold (95%CI 1.3-9.0) higher risk for post-operative bleeding, whereas the risks for wound complications and thromboembolic events were comparable between groups. The higher bleeding risk was driven by a higher rate of extraspinal haematomas (3.3% vs. 0.6%; p = 0.001), while there was no difference in epidural haematomas and haematoma evacuations. Risk factors for adverse events among patients with OAC were mechanical heart valves, posterior neck surgery, blood loss > 1000 mL, age, female sex, BMI > 30 kg/m2 and post-operative PTT levels. At 3-month follow-up, most patients reported favourable outcomes with no difference between groups. CONCLUSION: Although OAC patients have a higher risk for complications after spine surgery, the risk for major events is low and patients benefit similarly from surgery.

Assessment of malalignment at early stage in adolescent idiopathic scoliosis: a longitudinal cohort study.

INTRODUCTION: Our objective was to assess abnormalities of the odontoid-hip axis (OD-HA) angle in a mild scoliotic population to determine whether screening for malalignment would help predict the distinction between progressive and stable adolescent idiopathic scoliosis (AIS) at early stage. MATERIALS AND METHODS: All patients (non-scoliotic and AIS) underwent a biplanar X-ray between 2013 and 2020. In AIS, inclusion criteria were Cobb angle between 10° and 25°; Risser sign lower than 3; age higher than 10 years; and no previous treatment. A 3D spine reconstruction was performed, and the OD-HA was computed automatically. A reference corridor for OD-HA values in non-scoliotic subjects was calculated as the range [5th-95th percentiles]. A severity index, helping to distinguish stable and progressive AIS, was calculated and weighted according to the OD-HA value. RESULTS: Eighty-three non-scoliotic and 205 AIS were included. The mean coronal and sagittal OD-HA angles in the non-scoliotic group were 0.2° and -2.5°, whereas in AIS values were 0.3° and -0.8°, respectively. For coronal and sagittal OD-HA, 27.5% and 26.8% of AIS were outside the reference corridor compared with 10.8% in non-scoliotic (OR = 3.1 and 3). Adding to the severity index a weighting factor based on coronal OD-HA, for thoracic scoliosis, improved the positive predictive value by 9% and the specificity by 13%. CONCLUSION: Analysis of OD-HA suggests that AIS patients are almost three times more likely to have malalignment compared with a non-scoliotic population. Furthermore, analysis of coronal OD-HA is promising to help the clinician distinguish between stable and progressive thoracic scoliosis.

Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease.

BACKGROUND: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). METHODS: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity. RESULTS: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. CONCLUSIONS: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.

Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.

PURPOSE: Radiation-free systems based on dorsal surface topography can potentially represent an alternative to radiographic examination for early screening of scoliosis, based on the ability of recognizing the presence of deformity or classifying its severity. This study aims to assess the effectiveness of a deep learning model based on convolutional neural networks in directly predicting the Cobb angle from rasterstereographic images of the back surface in subjects with adolescent idiopathic scoliosis. METHODS: Two datasets, comprising a total of 900 individuals, were utilized for model training (720 samples) and testing (180). Rasterstereographic scans were performed using the Formetric4D device. The true Cobb angle was obtained from radiographic examination. The best model configuration was identified by comparing different network architectures and hyperparameters through cross-validation in the training set. The performance of the developed model in predicting the Cobb angle was assessed on the test set. The accuracy in classifying scoliosis severity (non-scoliotic, mild, and moderate category) based on Cobb angle was evaluated as well. RESULTS: The mean absolute error in predicting the Cobb angle was 6.1° ± 5.0°. Moderate correlation (r = 0.68) and a root-mean-square error of 8° between the predicted and true values was reported. The overall accuracy in classifying scoliosis severity was 59%. CONCLUSION: Despite some improvement over previous approaches that relied on spine shape reconstruction, the performance of the present fully automatic application is below that of radiographic evaluation performed by human operators. The study confirms that rasterstereography cannot be considered a valid non-invasive alternative to radiographic examination for clinical purposes.

Developing a new tool for scoliosis screening in a tertiary specialistic setting using artificial intelligence: a retrospective study on 10,813 patients: 2023 SOSORT award winner.

PURPOSE: The study aims to assess if the angle of trunk rotation (ATR) in combination with other readily measurable clinical parameters allows for effective non-invasive scoliosis screening. METHODS: We analysed 10,813 patients (4-18 years old) who underwent clinical and radiological evaluation for scoliosis in a tertiary clinic specialised in spinal deformities. We considered as predictors ATR, Prominence (mm), visible asymmetry of the waist, scapulae and shoulders, familiarity, sex, BMI, age, menarche, and localisation of the curve. We implemented a Logistic Regression model to classify the Cobb angle of the major curve according to thresholds of 15, 20, 25, 30, and 40 degrees, by randomly splitting the dataset into 80-20% for training and testing, respectively. RESULTS: The model showed accuracies of 74, 81, 79, 79, and 84% for 15-, 20-, 25-, 30- and 40-degrees thresholds, respectively. For all the thresholds ATR, Prominence, and visible asymmetry of the waist were the top five most important variables for the prediction. Samples that were wrongly classified as negatives had always statistically significant (p ≪ 0.01) lower values of ATR and Prominence. This confirmed that these two parameters were very important for the correct classification of the Cobb angle. The model showed better performances than using the 5 and 7 degrees ATR thresholds to prescribe a radiological examination. CONCLUSIONS: Machine-learning-based classification models have the potential to effectively improve the non-invasive screening for AIS. The results of the study constitute the basis for the development of easy-to-use tools enabling physicians to decide whether to prescribe radiographic imaging.

Deep phenotyping the cervical spine: automatic characterization of cervical degenerative phenotypes based on T2-weighted MRI.

PURPOSE: Radiological degenerative phenotypes provide insight into a patient's overall extent of disease and can be predictive for future pathological developments as well as surgical outcomes and complications. The objective of this study was to develop a reliable method for automatically classifying sagittal MRI image stacks of cervical spinal segments with respect to these degenerative phenotypes. METHODS: We manually evaluated sagittal image data of the cervical spine of 873 patients (5182 motion segments) with respect to 5 radiological phenotypes. We then used this data set as ground truth for training a range of multi-class multi-label deep learning-based models to classify each motion segment automatically, on which we then performed hyper-parameter optimization. RESULTS: The ground truth evaluations turned out to be relatively balanced for the labels disc displacement posterior, osteophyte anterior superior, osteophyte posterior superior, and osteophyte posterior inferior. Although we could not identify a single model that worked equally well across all the labels, the 3D-convolutional approach turned out to be preferable for classifying all labels. CONCLUSIONS: Class imbalance in the training data and label noise made it difficult to achieve high predictive power for underrepresented classes. This shortcoming will be mitigated in the future versions by extending the training data set accordingly. Nevertheless, the classification performance rivals and in some cases surpasses that of human raters, while speeding up the evaluation process to only require a few seconds.

Association between sagittal alignment and loads at the adjacent segment in the fused spine: a combined clinical and musculoskeletal modeling study of 205 patients with adult spinal deformity.

PURPOSE: Sagittal malalignment is a risk factor for mechanical complications after surgery for adult spinal deformity (ASD). Spinal loads, modulated by sagittal alignment, may explain this relationship. The aims of this study were to investigate the relationships between: (1) postoperative changes in loads at the proximal segment and realignment, and (2) absolute postoperative loads and postoperative alignment measures. METHODS: A previously validated musculoskeletal model of the whole spine was applied to study a clinical sample of 205 patients with ASD. Based on clinical and radiographic data, pre-and postoperative patient-specific alignments were simulated to predict loads at the proximal segment adjacent to the spinal fusion. RESULTS: Weak-to-moderate associations were found between pre-to-postop changes in lumbar lordosis, LL (r = - 0.23, r = - 0.43; p < 0.001), global tilt, GT (r = 0.26, r = 0.38; p < 0.001) and the Global Alignment and Proportion score, GAP (r = 0.26, r = 0.37; p < 0.001), and changes in compressive and shear forces at the proximal segment. GAP score parameters, thoracic kyphosis measurements and the slope of upper instrumented vertebra were associated with changes in shear. In patients with T10-pelvis fusion, moderate-to-strong associations were found between postoperative sagittal alignment measures and compressive and shear loads, with GT showing the strongest correlations (r = 0.75, r = 0.73, p < 0.001). CONCLUSIONS: Spinal loads were estimated for patient-specific full spinal alignment profiles in a large cohort of patients with ASD pre-and postoperatively. Loads on the proximal segments were greater in association with sagittal malalignment and malorientation of proximal vertebra. Future work should explore whether they provide a causative mechanism explaining the associated risk of proximal junction complications.

An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.

PURPOSE: Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. METHODS: A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. RESULTS: The median Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network, although occasional failures were noted. Cross-sectional area and fat fraction of the muscles were in agreement with published data. CONCLUSIONS: The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in an accurate and fully automated manner, although it is not without limitations. The model is therefore a suitable research tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

Artificial intelligence in spine care: current applications and future utility.

PURPOSE: The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. METHODS: A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review. RESULTS: This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems. CONCLUSION: Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.

BACKGROUND: As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. PURPOSE: The following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research. METHODS: A multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed. RESULTS: Overall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised. CONCLUSIONS: Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.

Measuring the critical shoulder angle on radiographs: an accurate and repeatable deep learning model.

PURPOSE: Since the critical shoulder angle (CSA) is considered a risk factor for shoulder pathology and the intra- and inter-rater variabilities in its calculation are not negligible, we developed a deep learning model that calculates it automatically and accurately. METHODS: We used a dataset of 8467 anteroposterior x-ray images of the shoulder annotated with 3 landmarks of interest. A Convolutional Neural Network model coupled with a spatial to numerical transform (DSNT) layer was used to predict the landmark coordinates from which the CSA was calculated. The performances were evaluated by calculating the Euclidean distance between the ground truth coordinates and the predicted ones normalized with respect to the distance between the first and the second points, and the error between the CSA angle measured by a human observer and the predicted one. RESULTS: Regarding the normalized point distances, we obtained a median error of 2.9%, 2.5%, and 2% for the three points among the entire set. Considering CSA calculations, the median errors were 1° (standard deviation 1.2°), 0.88° (standard deviation 0.87°), and 0.99° (standard deviation 1°) for angles below 30°, between 30° and 35°, and above 35°, respectively. DISCUSSION: These results demonstrate that the model has the potential to be used in clinical settings where the replicability is important. The reported standard error of the CSA measurement is greater than 2° that is above the median error of our model, indicating a potential accuracy sufficient to be used in a clinical setting.

Artificial Intelligence Accurately Detects Traumatic Thoracolumbar Fractures on Sagittal Radiographs.

Background and Objectives: Commonly being the first step in trauma routine imaging, up to 67% fractures are missed on plain radiographs of the thoracolumbar (TL) spine. The aim of this study was to develop a deep learning model that detects traumatic fractures on sagittal radiographs of the TL spine. Identifying vertebral fractures in simple radiographic projections would have a significant clinical and financial impact, especially for low- and middle-income countries where computed tomography (CT) and magnetic resonance imaging (MRI) are not readily available and could help select patients that need second level imaging, thus improving the cost-effectiveness. Materials and Methods: Imaging studies (radiographs, CT, and/or MRI) of 151 patients were used. An expert group of three spinal surgeons reviewed all available images to confirm presence and type of fractures. In total, 630 single vertebra images were extracted from the sagittal radiographs of the 151 patients-302 exhibiting a vertebral body fracture, and 328 exhibiting no fracture. Following augmentation, these single vertebra images were used to train, validate, and comparatively test two deep learning convolutional neural network models, namely ResNet18 and VGG16. A heatmap analysis was then conducted to better understand the predictions of each model. Results: ResNet18 demonstrated a better performance, achieving higher sensitivity (91%), specificity (89%), and accuracy (88%) compared to VGG16 (90%, 83%, 86%). In 81% of the cases, the "warm zone" in the heatmaps correlated with the findings, suggestive of fracture within the vertebral body seen in the imaging studies. Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. A4, A3, and A1 were the most frequent fracture types according to the AO Spine Classification. Conclusions: ResNet18 could accurately identify the traumatic vertebral fractures on the TL sagittal radiographs. In most cases, the model based its prediction on the same areas that human expert classifiers used to determine the presence of a fracture.

Effect of curve location on the severity index for adolescent idiopathic scoliosis: a longitudinal cohort study.

OBJECTIVES: Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children. A severity index was recently proposed to identify the stable from the progressive scoliosis at the first standardized biplanar radiographic exam. The aim of this work was to extend the validation of the severity index and to determine if curve location influences its predictive capabilities. METHODS: AIS patients with Cobb angle between 10° and 25°, Risser 0-2, and no previous treatment were included. They underwent standing biplanar radiography and 3D reconstruction of the spine and pelvis, which allowed to calculate their severity index. Patients were grouped by curve location (thoracic, thoracolumbar, lumbar). Patients were followed up until skeletal maturity (Risser ≥ 3) or brace prescription. Their outcome was compared to the prediction made by the severity index. RESULTS: In total, 205 AIS patients were included; 82% of them (155/189, 95% confidence interval [74-90%]) were correctly classified by the index, while 16 patients were unclassified. Positive predictive ratio was 78% and negative predictive ratio was 86%. Specificity (78%) was not significantly affected by curve location, while patients with thoracic and lumbar curves showed higher sensitivity (≥ 89%) than those with thoracolumbar curves (74%). CONCLUSIONS: In this multicentric cohort of 205 patients, the severity index was used to predict the risk of progression from mild to moderate scoliosis, with similar results of typical major curve types. This index represents a novel tool to aid the clinician and the patient in the modulation of the follow-up and, for progressive patients, their decision for brace treatment. KEY POINTS: • The severity index of adolescent idiopathic scoliosis has the potential to detect patients with progressive scoliosis as early as the first exam. • Out of 205 patients, 82% were correctly classified as either stable or progressive by the severity index. • The location of the main curve had small effect on the predictive capability of the index.

Appendicular Muscle Mass, Thigh Intermuscular Fat Infiltration, and Risk of Fall in Postmenopausal Osteoporotic Elder Women.

BACKGROUND: The association between the quantity and composition of skeletal muscle and the decline in physical function in elderly is poorly understood. Therefore, the primary aim of this cross-over study was to investigate the association between thigh intermuscular adipose tissue (IMAT) infiltration, appendicular muscle mass, and risk of fall in postmenopausal osteoporotic elder women. Second, we examined the differences in muscle mass, IMAT, and risk of fall in the same sample of older subjects after being classified as sarcopenic or nonsarcopenic on the basis of the dual-energy X-ray absorptiometry (DXA)-based Appendicular Skeletal Muscle Mass Index (ASMMI). METHODS: Twenty-nine subjects (age: 72.4 ± 6.8; BMI: 23.0 ± 3.3; and T-score: -2.7 ± 0.2) completed the following clinical evaluations: (1) whole-body DXA to assess the ASMMI; (2) magnetic resonance to determine the cross-sectional muscle area (CSA) and IMAT of thigh muscles, expressed both in absolute (IMATabs) and relative (IMATrel) values; and (3) risk of fall assessment through the OAK system (Khymeia, Noventa Padovana, Italy). The existence of a correlation between the risk of fall (OAK scores, an automated version of the Brief-BESTest) and the clinical parameters (ASMMI, CSA, IMATrel, and IMATabs) was tested by the Pearson's correlation index while data homogeneity between sarcopenic and nonsarcopenic subjects was tested through unpaired Student t tests or with the Mann-Whitney rank test. Effect sizes (ES) were used to determine the magnitude of the effect for all significant outcomes. RESULTS: Eleven subjects were classified as sarcopenic and 18 as nonsarcopenic based on their ASMMI (cutoff value: 5.5 kg/m2). A positive correlation between OAK and CSA was observed (r2 = 0.19; p = 0.033), whereas a negative correlation between OAK and IMATrel was detected (r2 = 0.27; p = 0.009). No correlations were observed between OAK and ASMMI and between ASMMI and IMATrel. Sarcopenic subjects showed significantly lower weight (p = 0.002; ES = 1.30, large), BMI (p = 0.0003; ES = 1.82, large), CSA (p = 0.010; ES = 1.17, moderate), and IMATabs (p = 0.022; ES = 1.63, large) than nonsarcopenic individuals, whereas OAK scores and IMATrel were similar between groups. DISCUSSION/CONCLUSION: Increased IMAT and lower CSA in the thigh muscles are associated with higher risk of fall while ASMMI, a value of appendicular muscle mass, was not associated with physical performance in older adults.

Expected impact of lockdown measures due to COVID-19 on disabling conditions: a modelling study of chronic low back pain.

PURPOSE: The COVID-19 pandemic and the containment measures such as social distancing, mobility restrictions and quarantine have significantly impacted the delivery of healthcare services, with possible negative effects on low back pain patients. In this study, we used an innovative agent-based model to quantify the effects of COVID-19 on the prevalence and severity of low back pain in the general population. METHODS: Epidemiological data were used to simulate the low back pain evolution in a population of 300,000 agents. Reduced access to treatment due to the containment measures was simulated with a probabilistic approach, in which 500 random scenarios (differing in: length of the lockdown, probability of having access to treatment, time before the resumption of treatment, duration of the effects of the treatment after its interruption) were simulated. RESULTS: The lockdown may increase the mean pain score higher than 1/10 points for patients suffering from acute low back pain, up to 4-5/10 points for specific individuals. The lockdown also affected the length of pain episodes, possibly impacting chronicity and disability. All the variables describing the random scenarios had a relevant impact in determining both the increase of pain intensity in the population and the length of the effects of the lockdown. CONCLUSIONS: "Optimal lockdown parameters" which minimize the impact on low back pain while preserving the effects on infection spread and mortality could not be identified. Policies favouring a prompt resumption of treatments after the lockdown may be effective in shortening the duration of its negative effects.

ISSLS Prize in Bioengineering Science 2021: in vivo sagittal motion of the lumbar spine in low back pain patients-a radiological big data study.

PURPOSE: We investigated the flexion-extension range of motion and centre of rotation of lumbar motion segments in a large population of 602 patients (3612 levels), and the associations between lumbar motion and other parameters such as sex, age and intervertebral disc degeneration. METHODS: Lumbar radiographs in flexion-extension of 602 patients suffering from low back pain and/or suspect instability were collected; magnetic resonance images were retrieved and used to score the degree of disc degeneration for a subgroup of 354 patients. Range of motion and centre of rotation were calculated for all lumbosacral levels with in-house software allowing for high degree of automation. Associations between motion parameters and age, sex, spinal level and disc degeneration were then assessed. RESULTS: The median range of motion was 6.6° (range 0.1-28.9°). Associations between range of motion and age as well as spinal level, but not sex, were found. Disc degeneration determined a consistent reduction in the range of motion. The centre of rotation was most commonly located at the centre of the lower endplate or slightly lower. With progressive degeneration, centres of rotation were increasingly dispersed with no preferential directions. CONCLUSION: This study constitutes the largest analysis of the in vivo lumbar motion currently available and covers a wide range of clinical scenarios in terms of age and degeneration. Findings confirmed that ageing determines a reduction in the mobility independently of degeneration and that in degenerative levels, centres of rotation are dispersed around the centre of the intervertebral space.

The impact of age, sex, disc height loss and T1 slope on the upper and lower cervical lordosis: a large-scale radiologic study.

PURPOSE: To clarify the relative influence of age, sex, disc height loss and T1 slope on upper (Occiput-C2) and lower cervical lordosis (C2-C7). METHODS: Standing lateral cervical radiographs of 865 adult subjects were evaluated. The presence and severity of disc height loss from C2/C3 to C6/C7 (a total of 4325 discs) were assessed using a validated grading system. The total disc height loss score for each subject was calculated as the sum of the score of each disc space. Sagittal radiographic parameters included: occipital slope, occiput-C2 (Oc-C2) lordosis, C2-C7 lordosis and T1 slope. Multivariable regression analyses were performed to examine the relative influence of the multiple factors on upper and lower cervical lordosis. RESULTS: This study included 360 males and 505 females, with a mean age of 40.2 ± 16.0 years (range, 20-95 years). Linear multivariate regression analyses showed that greater age, male sex, greater T1 slope were each found to be significantly and independently associated with greater C2-C7 lordosis, whereas total disc height loss score was negatively associated with C2-C7 lordosis. T1 slope had the most independent influence on C2-C7 lordosis among these factors. Age, sex and disc height loss were not independently associated with Oc-C2 lordosis. CONCLUSIONS: Results from our large-scale radiologic analysis may enhance the understanding of the factors that affect cervical lordosis, indicating that age, sex, disc height loss and T1 slope were each independently associated with C2-C7 lordosis. However, age, sex and disc height loss were not independently associated with upper cervical lordosis.

Radiographic cervical spine degenerative findings: a study on a large population from age 18 to 97 years.

PURPOSE: The aims of this study were (1) to determine the prevalence of radiographic cervical disc degeneration in a large population of patients aged from 18 to 97 years; (2) to investigate individually the prevalence and distribution of height loss, osteophyte formation, endplate sclerosis and spondylolisthesis; and (3) to describe the patterns of cervical disc degeneration. METHODS: A retrospective study was performed. Standard lateral cervical spine radiographs in standing, neutral position of 1581 consecutive patients (723 males, 858 females) with an average age of 41.2 ± 18.2 years were evaluated. Cervical disc degeneration was graded from C2/C3 to C6/C7 based on a validated quantitative grading system. The prevalence and distribution of radiographic findings were evaluated and associations with age were investigated. RESULTS: 53.9% of individuals had radiographic disc degeneration and the most affected level was C5/C6. The presence and severity of disc degeneration were found to be significantly associated with age both in male and female subjects. The most frequent and severe occurrences of height loss, osteophyte formation, and endplate sclerosis were at C5/C6, whereas spondylolisthesis was most observed at C4/C5. Age was significantly correlated with radiographic degenerative findings. Contiguous levels degeneration pattern was more likely found than skipped level degeneration. The number of degenerated levels was also associated with age. CONCLUSIONS: The presence and severity of radiographic disc degeneration increased with aging in the cervical spine. Older age was associated with greater number of degenerated disc levels. Furthermore, the correlations between age and the degree of degenerative findings were stronger at C5/C6 and C6/C7 than at other cervical spinal levels.

Classification of endplate lesions in the lumbar spine and association with risk factors, biochemistry, and genetics.

PURPOSE: To detect the associations between the degree of the endplate (EP) lesions with the presence of risk factors, biochemical and genetic markers previously observed in low back pain (LBP) patients with EP defects in comparison with hernia/discopathy patients and healthy controls. METHODS: In this observational retrospective study, T2-weighted sagittal MRI images (n = 223 LBP patients) were scored for EP lesions by two independent observers. Total MRI score and number of affected levels (L1/L2-L5/S1) have been considered for the correlation with demographic, behavioral, clinical, biochemical (25(OH)D, CTx-I and CTx-II levels, n = 69 males) and VDR variables. RESULTS: Males showed higher BMI and total MRI score than females. Patients bearing TT compared to tt VDR genotypes showed significant higher total MRI scores. Among males (n = 125), TT, bb and aa genotypes showed increased total MRI scores. Higher total MRI score directly correlates with higher levels of CTx-I and CTx-II (n = 69 males). CONCLUSIONS: The markers previously identified as associated with the presence of EP lesions have been confirmed as related to their severity and could be used to follow the pathology progression.