Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

The impact of novel inflammation-preserving treatment towards lumbar disc herniation resorption in symptomatic patients: a prospective, multi-imaging and clinical outcomes study.

PURPOSE: We performed a prospective one-year multi-imaging study to assess the clinical outcomes and rate of disc resorption in acute lumbar disc herniation (LDH) patients undergoing inflammation-preserving treatment (i.e. no NSAIDS, steroids). METHODS: All patients received gabapentin to relieve leg pain, 12 sessions of acupuncture. Repeat MRI was performed, every 3 months, after 12 sessions of treatment continued for those without 40% reduction in herniated disc sagittal area. Disc herniations sizes were measured on sagittal T2W MRI sequences, pre-treatment and at post-treatment intervals. Patients were stratified to fast, medium, slow, and prolonged recovery groups in relation to symptom resolution and disc resorption. RESULTS: Ninety patients (51% females; mean age: 48.6 years) were assessed. Mean size of disc herniation was 119.54 ± 54.34 mm2, and the mean VAS-Leg score was 6.12 ± 1.13 at initial presentation. A total of 19 patients (21.1%) improved at the time of the repeat MRI (i.e. within first 3 months post-treatment). 100% of all patient had LDH resorption within one year (mean: 4.4. months). There was no significant difference at baseline LDH between fast, medium, slow, and prolonged resorption groups. Initial LDH size was weakly associated with degree of leg pain at baseline and initial gabapentin levels. Surgery was avoided in all cases. CONCLUSION: This is the first study to note inflammation-preserving treatment, without conventional anti-inflammatory and steroid medications, as safe and effective for patients with an acute LDH. Rate of disc resorption (100%) was higher than comparative recent meta-analysis findings (66.7%) and no patient underwent surgery.

Discovery of circulating blood biomarkers in patients with and without Modic changes of the lumbar spine: a preliminary analysis.

PURPOSE: The following study aimed to determine the existence of blood biomarkers in symptomatic patients with or without lumbar Modic changes (MC). METHODS: A cross-sectional sub-analyses of a prospective cohort was performed. Fasting blood samples were collected from patients with and without lumbar MC who had undergone spinal fusion or microdiscectomy. An 80-plex panel and CCL5/RANTES were used to assess preoperative plasma cytokine concentrations. Patient demographics and imaging phenotypes were also assessed. RESULTS: Thirty-one subjects were analysed (n = 18 no MC; n = 13 MC). No significant differences were found in age, sex, body mass index, smoking and alcohol history, and surgical procedure (i.e. fusion, decompression) between the two groups (p > 0.05). Several statistically significant blood biomarkers in MC patients were identified, including elevated levels of C-C Motif Chemokine Ligand 5 (CCL5, p = 0.0006), while Macrophage Migration Inhibitory Factor (MIF) was significantly lower (p = 0.009). Additionally, C-X-C Motif Chemokine Ligand 5 (CXCL5, p = 0.052), Pentraxin 3 (PTX3, p = 0.06) and Galectin-3 (Gal-3, p = 0.07) showed potential relevance. Moreover, MC patients exhibited significantly higher levels of disc degeneration (p = 0.0001) and displacement severity (p = 0.020). Based on multivariate analyses and controlling for disc degeneration/displacement, CCL5 (OR 1.02; 95% CI 1.002-1.033; p = 0.028) and MIF (OR 0.60; 95% CI 0.382-0.951; p = 0.030) were independently associated with MC patients. CONCLUSION: This "proof-of-concept" study is the first to identify specific and significantly circulating blood biomarkers associated with symptomatic patients with lumbar MC, independent of disc alterations of degeneration and/or bulges/herniations. Specifically, differences in CCL5 and MIF protein levels were significantly noted in MC patients compared to those without MC.

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.

Is impaired lung function related to spinal deformities in patients with adolescent idiopathic scoliosis? A systematic review and meta-analysis-SOSORT 2019 award paper.

PURPOSE: Some teenagers with adolescent idiopathic scoliosis (AIS) display compromised lung function. However, the evidence regarding the relations between pulmonary impairments and various spinal deformity parameters in these patients remains unclear, which affects clinical management. This systematic review and meta-analysis aimed to summarize the associations between various lung function parameters and radiographic features in teenagers with AIS. METHODS: A search of PubMed, Embase, PEDro, SPORTDiscus, CINAHL, Cochrane Library, and PsycINFO (from inception to March 14, 2022) without language restriction. Original studies reporting the associations between lung function and spinal deformity in patients with AIS were selected. Independent reviewers extracted data and evaluated the methodological quality of the included studies according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Pearson correlation and 95% confidence intervals were calculated using random-effects meta-analysis. RESULTS: Twenty-seven studies involving 3162 participants were included. Limited-quality evidence supported that several spinal parameters were significantly related to lung function parameters (e.g., absolute value and percent of the predicted forced vital capacity (FVC; %FVC), forced expiratory volume in one second (FEV1; %FEV1), and total lung capacity (TLC; %TLC)) in AIS patients. Specifically, meta-analyses showed that main thoracic Cobb angles in the coronal plane were significantly and negatively related to FVC (r = - 0.245), %FVC (r = - 0.302), FEV1 (r = - 0.232), %FEV1 (r = - 0.348), FEV1/FVC ratio (r = - 0.166), TLC (r = - 0.302), %TLC (r = - 0.183), and percent predicted vital capacity (r = - 0.272) (p < 0.001). Similarly, thoracic apical vertebral rotation was negatively associated with %FVC (r = - 0.215) and %TLC (r = - 0.126) (p < 0.05). Conversely, thoracic kyphosis angles were positively related to %FVC (r = 0.180) and %FEV1 (r = 0.193) (p < 0.05). CONCLUSION: Larger thoracic Cobb angles, greater apical vertebral rotation angle, or hypokyphosis were significantly associated with greater pulmonary impairments in patients with AIS, although the evidence was limited. From a clinical perspective, the results highlight the importance of minimizing the three-dimensional spinal deformity in preserving lung function in these patients. More research is warranted to confirm these results.

Bullying, harassment, and discrimination of musculoskeletal researchers and the impact of the COVID-19 pandemic: an international study.

PURPOSE: Bullying, harassment, and discrimination (BHD) are prevalent in academic, scientific, and clinical departments, particularly orthopedic surgery, and can have lasting effects on victims. As it is unclear how BHD affects musculoskeletal (MSK) researchers, the following study assessed BHD in the MSK research community and whether the COVID-19 pandemic, which caused hardships in other industries, had an impact. METHODS: A web-based anonymous survey was developed in English by ORS Spine Section members to assess the impact of COVID-19 on MSK researchers in North America, Europe, and Asia, which included questions to evaluate the personal experience of researchers regarding BHD. RESULTS: 116 MSK researchers completed the survey. Of respondents, 34.5% (n = 40) focused on spine, 30.2% (n = 35) had multiple areas of interest, and 35.3% (n = 41) represented other areas of MSK research. BHD was observed by 26.7% (n = 31) of respondents and personally experienced by 11.2% (n = 13), with mid-career faculty both observing and experiencing the most BHD. Most who experienced BHD (53.8%, n = 7) experienced multiple forms. 32.8% (n = 38) of respondents were not able to speak out about BHD without fear of repercussions, with 13.8% (n = 16) being unsure about this. Of those who observed BHD, 54.8% (n = 17) noted that the COVID-19 pandemic had no impact on their observations. CONCLUSIONS: To our knowledge, this is the first study to address the prevalence and determinants of BHD among MSK researchers. MSK researchers experienced and observed BHD, while many were not comfortable reporting and discussing violations to their institution. The COVID-19 pandemic had mixed-effects on BHD. Awareness and proactive policy changes may be warranted to reduce/eliminate the occurrence of BHD in this community.

Differential Effects of the COVID-19 Pandemic on Physical Activity Involvements and Exercise Habits in People With and Without Chronic Diseases: A Systematic Review and Meta-analysis.

OBJECTIVE: To conduct a systematic review and meta-analysis to summarize evidence regarding differential changes in physical activity (PA) involvements and exercise habits in people with and without chronic diseases during the COVID-19 outbreak. DATA SOURCES: MEDLINE, Embase, SPORTDiscus, Cumulative Index to Nursing and Allied Health, PsycINFO, Cochrane Library, and Physiotherapy Evidence Database were searched from November 2019 to May 2021. STUDY SELECTION: Two reviewers independently screened cross-sectional and longitudinal studies that investigated changes in PA-related outcomes in people with and without chronic diseases during the pandemic. DATA EXTRACTION: PA-related outcomes and sedentary time were extracted from the included studies. Relevant risk of bias were assessed. Meta-analyses were conducted for each PA-related outcome, if applicable. Quality of evidence of each PA-related outcome was evaluated by Grading of Recommendations Assessment, Development, and Evaluation. DATA SYNTHESIS: Of 1226 identified citations, 36 articles (28 with and 8 without chronic diseases) with 800,256 participants were included. Moderate evidence from wearable sensors supported a significant reduction in pooled estimates of step count (standardized mean differences [SMD]=-2.79, P<.01). Very limited to limited evidence substantiated significant decreases in self-reported PA-related outcomes and significant increases in sedentary behaviors among people with and without chronic diseases. Specifically, pooled estimates of metabolic equivalent-minute per week (SMD=-0.16, P=.02) and PA duration (SMD=-0.07, P<.01) were significantly decreased, while sedentary time (SMD=0.09, P=.04) showed significant increases in the general population (small to large effects). Very limited evidence suggested no significant PA changes among people in a country without lockdown. CONCLUSIONS: During the pandemic, objective and self-reported assessments showed significant reductions in PA in people with and without chronic diseases globally. This mainly occurred in countries with lockdowns. Although many countries have adopted the "live with the coronavirus" policy, authorities should implement population-based strategies to revert the potential lockdown-related long-term deleterious effects on people's health.

Integrative analysis of metabolomic, genomic, and imaging-based phenotypes identify very-low-density lipoprotein as a potential risk factor for lumbar Modic changes.

PURPOSE: Modic changes (MC) on magnetic resonance imaging (MRI) have been associated with the development and severity of low back pain (LBP). The etiology of MC remains elusive, but it has been suggested that altered metabolism may be a risk factor. As such, this study aimed to identify metabolomic biomarkers for MC phenotypes of the lumbar spine via a combined metabolomic-genomic approach. METHODS: A population cohort of 3,584 southern Chinese underwent lumbar spine MRI. Blood samples were genotyped with single-nucleotide polymorphisms (SNP) arrays (n = 2,482) and serum metabolomics profiling using magnetic resonance spectroscopy (n = 757), covering 130 metabolites representing three molecular windows, were assessed. Genome-wide association studies (GWAS) were performed on each metabolite, to construct polygenic scores for predicting metabolite levels in subjects who had GWAS but not metabolomic data. Associations between predicted metabolite levels and MC phenotypes were assessed using linear/logistic regression and least absolute shrinkage and selection operator (LASSO). Two-sample Mendelian randomization analysis tested for causal relationships between metabolic biomarkers and MC. RESULTS: 20.4% had MC (10.6% type 1, 67.2% type 2, 22.2% mixed types). Significant MC metabolomic biomarkers were mean diameter of very-low-density lipoprotein (VLDL)/low-density lipoprotein (LDL) particles and cholesterol esters/phospholipids in large LDL. Mendelian randomization indicated that decreased VLDL mean diameter may lead to MC. CONCLUSIONS: This large-scale study is the first to address metabolomics in subject with/without lumbar MC. Causality studies implicate VLDL related to MC, noting a metabolic etiology. Our study substantiates the field of "spino-metabolomics" and illustrates the power of integrating metabolomics-genomics-imaging phenotypes to discover biomarkers for spinal disorders, paving the way for more personalized spine care for patients.

ISSLS PRIZE in Clinical Science 2022: Epidemiology, risk factors and clinical impact of juvenile Modic changes in paediatric patients with low back pain.

PURPOSE: It's a long-held belief that Modic changes (MC) occur only in adults, with advanced age, and are highly associated with pain and adverse outcomes. The following study addressed the epidemiology, risk factors and clinical relevance of MC in young paediatric patients. METHODS: Two hundred and seven consecutive patients with no history of deformities, neoplasms, trauma, or infections were included in this ambispective study. MRIs were utilized to assess MCs and types, and other degenerative disc/endplate abnormalities. Subject demographics, duration of symptoms, clinic visits, conservative management (physical therapy, NSAIDs, opioids, injections) and surgery were noted. RESULTS: The mean age was 16.5 years old (46.9% males), 14% had MCs and they occurred throughout the spine. Subject baseline demographics were similar between MCs and non-MCs patients (p > 0.05). Modic type 2 (50%) was the most common type (type 1:27.1%; type 3:18.8%; mixed:4.7%). Multivariate analyses noted that endplate damage (OR: 11.36), disc degeneration (OR: 5.81), disc space narrowing (OR: 5.77), Schmorl's nodes (OR: 4.30) and spondylolisthesis (OR: 3.55) to be significantly associated with MCs (p < 0.05). No significant differences in conservative management were noted between Modic and non-MCs patients (p > 0.05). Among surgery patients (n = 44), 21% also had MCs (p = 0.134). Symptom-duration was significantly greater in MC patients (p = 0.049). CONCLUSION: Contrary to traditional dogma, robust evidence now exists noting that MCs and their types can develop in children. Our findings give credence to the "Juvenile" variant of MCs, whereby its implications throughout the lifespan need to be assessed. Juvenile MCs have prolonged symptoms and related to specific structural spine phenotypes.

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.

Learning-based fully automated prediction of lumbar disc degeneration progression with specified clinical parameters and preliminary validation.

BACKGROUND: Lumbar disc degeneration (LDD) may be related to aging, biomechanical and genetic factors. Despite the extensive work on understanding its etiology, there is currently no automated tool for accurate prediction of its progression. PURPOSE: We aim to establish a novel deep learning-based pipeline to predict the progression of LDD-related findings using lumbar MRIs. MATERIALS AND METHODS: We utilized our dataset with MRIs acquired from 1,343 individual participants (taken at the baseline and the 5-year follow-up timepoint), and progression assessments (the Schneiderman score, disc bulging, and Pfirrmann grading) that were labelled by spine specialists with over ten years clinical experience. Our new pipeline was realized by integrating the MRI-SegFlow and the Visual Geometry Group-Medium (VGG-M) for automated disc region detection and LDD progression prediction correspondingly. The LDD progression was quantified by comparing the Schneiderman score, disc bulging and Pfirrmann grading at the baseline and at follow-up. A fivefold cross-validation was conducted to assess the predictive performance of the new pipeline. RESULTS: Our pipeline achieved very good performances on the LDD progression prediction, with high progression prediction accuracy of the Schneiderman score (Accuracy: 90.2 ± 0.9%), disc bulging (Accuracy: 90.4% ± 1.1%), and Pfirrmann grading (Accuracy: 89.9% ± 2.1%). CONCLUSION: This is the first attempt of using deep learning to predict LDD progression on a large dataset with 5-year follow-up. Requiring no human interference, our pipeline can potentially achieve similar predictive performances in new settings with minimal efforts.

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.

Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion.

PURPOSE: Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF. METHODS: Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance. RESULTS: In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features. CONCLUSIONS: Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.

Pediatric Back Pain: A Scoring System to Guide Use of Magnetic Resonance Imaging.

BACKGROUND: The prevalence of back pain in the pediatric population is increasing, and the workup of these patients presents a clinical challenge. Many cases are selflimited, but failure to diagnose a pathology that requires clinical intervention can carry severe repercussions. Magnetic resonance imaging (MRI) carries a high cost to the patient and health care system, and may even require procedural sedation in the pediatric population. The aim of this study was to develop a scoring system based on pediatric patient factors to help determine when an MRI will change clinical management. METHODS: This is a retrospective cohort analysis of consecutive pediatric patients who presented to clinic with a chief complaint of back pain between 2010 and 2018 at single orthopaedic surgery practice. Comprehensive demographic and presentation variables were collected. A predictive model of factors that influence whether MRI results in a change in management was then generated using cross-validation least absolute shrinkage and selection operator logistic regression analysis. RESULTS: A total of 729 patients were included, with a mean age of 15.1 years (range: 3 to 20 y). Of these, 344 (47.2%) had an MRI. A predictive model was generated, with nocturnal symptoms (5 points), neurological deficit (10 points), age (0.7 points per year), lumbar pain (2 points), sudden onset of pain (3.25 points), and leg pain (3.75 points) identified as significant predictors. A combined score of greater than 9.5 points for a given patient is highly suggestive that an MRI will result in a change in clinical management (specificity: 0.93; positive predictive value: 0.92). CONCLUSIONS: A predictive model was generated to help determine when ordering an MRI may result in a change in clinical management for workup of back pain in the pediatric population. The main factors included the presence of a neurological deficit, nocturnal symptoms, sudden onset, leg pain, lumbar pain, and age. Care providers can use these findings to better determine if and when an MRI might be appropriate. LEVEL OF EVIDENCE: Level III-diagnostic study.

The past, present, and future of remote patient monitoring in spine care: an overview.

PURPOSE: Remote patient monitoring (RPM) has revolutionized the landscape of healthcare. From humble beginnings rooted in landline home telephone calls to present-day devices with near instantaneous wireless connectivity, the evolution of technology has ushered in an era of digital medicine and remote care. Presently, a vast array of healthcare data points can be automatically generated, analyzed, and forwarded to providers to supplement clinical decision-making. While RPM originated and was popularized within medicine, its role in orthopedics, and particularly within spine surgery, is evolving. We sought to provide an overview of RPM within orthopedics, with specific attention on spine care, analyzing its origins, present-day form, and prospects. METHODS: We reviewed the literature to date as it pertains to RPM within healthcare at large, orthopedics, and spine care. RESULTS: We detail the development and clinical use of wearable technology and smart implants, examining the underlying technology and evaluating the spectrum of their present-day and potential applications. CONCLUSIONS: Technological advancements are not only reshaping the paradigm of musculoskeletal care but are also redefining the physician-patient relationship as well as reimagining traditional perspectives on healthcare data collection and privacy.

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.

Artificial intelligence predicts disk re-herniation following lumbar microdiscectomy: development of the "RAD" risk profile.

PURPOSE: Surgical treatment of herniated lumbar intervertebral disks is a common procedure worldwide. However, recurrent herniated nucleus pulposus (re-HNP) may develop, complicating outcomes and patient management. The purpose of this study was to utilize machine-learning (ML) analytics to predict lumbar re-HNP, whereby a personalized risk prediction can be developed as a clinical tool. METHODS: A retrospective, single center study was conducted of 2630 consecutive patients that underwent lumbar microdiscectomy (mean follow-up: 22-months). Various preoperative patient pain/disability/functional profiles, imaging parameters, and anthropomorphic/demographic metrics were noted. An Extreme Gradient Boost (XGBoost) classifier was implemented to develop a predictive model identifying patients at risk for re-HNP. The model was exported to a web application software for clinical utility. RESULTS: There were 1608 males and 1022 females, 114 of whom experienced re-HNP. Primary herniations were central (65.8%), paracentral (17.6%), and far lateral (17.1%). The XGBoost algorithm identified multiple re-HNP predictors and was incorporated into an open-access web application software, identifying patients at low or high risk for re-HNP. Preoperative VAS leg, disability, alignment parameters, elevated body mass index, symptom duration, and age were the strongest predictors. CONCLUSIONS: Our predictive modeling via an ML approach of our large-scale cohort is the first study, to our knowledge, that has identified significant risk factors for the development of re-HNP after initial lumbar decompression. We developed the re-herniation after decompression (RAD) profile index that has been translated into an online screening tool to identify low-high risk patients for re-HNP. Additional validation is needed for potential global implementation.

COVID-19 and the rise of virtual medicine in spine surgery: a worldwide study.

PURPOSE: The COVID-19 pandemic forced many surgeons to adopt "virtual medicine" practices, defined as telehealth services for patient care and online platforms for continuing medical education. The purpose of this study was to assess spine surgeon reliance on virtual medicine during the pandemic and to discuss the future of virtual medicine in spine surgery. METHODS: A comprehensive survey addressing demographic data and virtual medicine practices was distributed to spine surgeons worldwide between March 27, 2020, and April 4, 2020. RESULTS: 902 spine surgeons representing seven global regions responded. 35.6% of surgeons were identified as "high telehealth users," conducting more than half of clinic visits virtually. Predictors of high telehealth utilization included working in an academic practice (OR = 1.68, p = 0.0015) and practicing in Europe/North America (OR 3.42, p < 0.0001). 80.1% of all surgeons were interested in online education. Dedicating more than 25% of one's practice to teaching (OR = 1.89, p = 0.037) predicted increased interest in online education. 26.2% of respondents were identified as "virtual medicine surgeons," defined as surgeons with both high telehealth usage and increased interest in online education. Living in Europe/North America and practicing in an academic practice increased odds of being a virtual medicine surgeon by 2.28 (p = 0.002) and 1.15 (p = 0.0082), respectively. 93.8% of surgeons reported interest in a centralized platform facilitating surgeon-to-surgeon communication. CONCLUSION: COVID-19 has changed spine surgery by triggering rapid adoption of virtual medicine practices. The demonstrated global interest in virtual medicine suggests that it may become part of the "new normal" for surgeons in the post-pandemic era.