The efficacy and safety of oral antibiotic treatment in patients with chronic low back pain and Modic changes: A systematic review and meta-analysis.

Background: This systematic review and meta-analysis aimed to summarize evidence regarding the effectiveness and safety of oral antibiotic intervention for chronic low back pain (CLBP) patients with/without type-1 Modic changes (MC1). Methods: AMED, CINAHL, Cochrane Library, Embase, and Medline were searched from inception to March 3, 2023. Randomized controlled trials (RCTs) or non-RCTs that investigated the effectiveness or safety of oral antibiotics in treating CLBP patients were eligible for inclusion. Two independent reviewers screened abstracts, full-text articles, and extracted data. The methodological quality of each included article were evaluated by RoB2 and NIH quality assessment tools. The quality of evidence was appraised by GRADE. Meta-analyses were performed, where applicable. A subgroup analysis was conducted to evaluate the RCTs and case series separately, and to evaluate the effect of removing a low-quality RCT. Results: Three RCTs and four case series were included. All Amoxicillin-clavulanate/Amoxicillin treatments lasted for approximately 3 months. Moderate- and low-quality evidence suggested that antibiotic was significantly better than placebo in improving disability and quality of life in CLBP patients with MC1 at 12-month follow-up, respectively. Low-quality evidence from meta-analyses of RCTs showed that oral antibiotic was significantly better than placebo in improving pain and disability in CLBP patients with MC1 immediately post-treatment. Very low-quality evidence from the case series suggested that oral Amoxicillin-clavulanate significantly improved LBP/leg pain, and LBP-related disability. Conversely, low-quality evidence found that oral Amoxicillin alone was not significantly better than placebo in improving global perceived health in patients with CLBP at the 12-month follow-up. Additionally, oral antibiotic users had significantly more adverse effects than placebo users. Conclusions: Although oral antibiotics were statistically superior to placebo in reducing LBP-related disability in patients with CLBP and concomitant MC1, its clinical significance remains uncertain. Future large-scale high-quality RCTs are warranted to validate the effectiveness of antibiotics in individuals with CLBP.

Determinants of Postoperative Compliance of Patient-Reported Outcome Assessments following Lumbar Spine Surgery.

Introduction: This study aimed to identify demographic, clinical, and operative factors associated with increased postoperative compliance of patient-reported outcome (PRO) assessments following lumbar spine surgery. Methods: A retrospective study of prospectively collected data of 1,680 consecutive adult patients who underwent elective lumbar surgery at a single institution from 2017-2020. Digital assessment questionnaires were used to assess PROs (i.e., VAS-back, VAS-leg, Oswestry Disability Index, Short Form (SF-12) mental & physical health, VR-12 mental and physical, and VR6D scores) and patient compliance, defined as the percentage of questionnaires completed preoperatively, at 3 months and 1 year after surgery. Multivariate logistic regression was used to assess the association between PRO compliance and patient characteristics. Results: A total of 1,680 patients (53.1% male, mean age: 57.7 years) had a mean PRO compliance of 64.7%. Compliance decreased continuously from initial preoperative rates (84.5%) to lower rates at 3 months (54.4%) and 12 months (45.6%), respectively, with 33.2% of patients completing zero assessment questionnaires at 12 months, postoperatively. Factors associated with significantly increased PRO compliance included being employed (preop: odds ratio [OR]=2.58, p=0.002; 3-month postop: OR=1.25, p=0.095; 12-month postop: OR=1.34, p=0.028). Factors associated with decreased compliance included preoperative smoking status (3-month postop: OR=0.63, p=0.029; 12-month postop: OR=0.60, p=0.016). Conclusions: Patients who completed greater than 50% of their PROs demonstrated significantly different rates of being employed compared with those who completed less than 50% throughout 1 year of follow-up. Preoperative smoking status was associated with decreased compliance, whereas a history of employment was associated with increased compliance throughout follow-up. To validate our findings and explore additional parameters that affect postoperative compliance of PROs, further investigation is required.

Resorption of Lumbar Disk Herniation: Mechanisms, Clinical Predictors, and Future Directions.

INTRODUCTION: Resorption after lumbar disk herniation is a common yet unpredictable finding. It is hypothesized that nearly 70% of lumbar herniated nucleus pulposus (HNP) undergo the resorption to a significant degree after acute herniation, which has led to nonoperative management before surgical planning. METHODS: This narrative review on the literature from 4 databases (MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Scopus, and Cochrane) examines historical and recent advancements related to disk resorption. Studies were appraised for their description of the predictive factor (e.g., imaging or morphologic factors), pathophysiology, and treatment recommendations. OBSERVATIONS: We reviewed 68 articles considering the possibility of resorption of lumbar HNP. Recent literature has proposed various mechanisms (inflammation and neovascularization, dehydration, and mechanical traction) of lumbar disk resorption; however, consensus has yet to be established. Current factors that increase the likelihood of resorption include the initial size of the herniation, sequestration, percentage of rim enhancement on initial gadolinium-based magnetic resonance imaging (MRI), composition of inflammatory mediators, and involvement of the posterior longitudinal ligament. CONCLUSION: Heterogeneity in imaging and morphologic factors has led to uncertainty in the identification of which lumbar herniations will resorb. Current factors that increase the likelihood of disk resorption include the initial size of the herniation, sequestration, percentage of rim enhancement on initial MRI, composition of cellular and inflammatory mediators present, and involvement of the posterior longitudinal ligament. This review article highlights the role of disk resorption after herniation without surgical intervention and questions the role of traditional noninflammatory medications after acute herniation. Further research is warranted to refine the ideal patient profile for disk resorption to ultimately avoid unnecessary treatment, thus individualizing patient care.

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.

Quantum Computing: The Future of Big Data and Artificial Intelligence in Spine.

With the emergence of big data and more personalized approaches to spine care and predictive modeling, data science and deep analytics are taking center-stage. Although current techniques in machine learning and artificial intelligence have gained attention, their applications remain limited by their reliance on traditional analytic platforms. Quantum computing has the ability to circumvent such constraints by attending to the various complexities of big data while minimizing space and time dimensions within computational algorithms. In doing so, quantum computing may one day address research and clinical objectives that currently cannot be tackled. Understanding quantum computing and its potential to improve patient management and outcomes is therefore imperative to drive further advancements in the spine field for the next several decades.

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.

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 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.

Assessing the Quality and Credibility of Publicly Available Videos on Cervical Fusion: Is YouTube a Reliable Educational Tool?

BACKGROUND: YouTube has become a popular source for patient education, though there are concerns regarding the quality and reliability of videos related to orthopaedic and neurosurgical procedures. This study aims to evaluate the credibility and educational content of videos on YouTube related to cervical fusion. Secondarily, the study aims to identify factors predictive of higher or lower quality videos. METHODS: A YouTube query using the search terms "cervical fusion" was performed, and the first 50 videos were included for analysis. Reliability was assessed using the Journal of the American Medical Association (JAMA) criteria. Educational quality was assessed using the Global Quality Score (GQS) and the Cervical Fusion Content Score (CFCS). Videos were stratified by content and source, and differences in JAMA, GQS, and CFCS scores were assessed. Multivariable linear regression was used to identify predictors of higher or lower JAMA, GQS, and CFCS scores. Statistical significance was established at P < 0.05. RESULTS: Total number of views was 6 221 816 with a mean of 124 436.32 ± 412 883.32 views per video. Physicians, academic, and medical sources had significantly higher mean JAMA scores (P = 0.042). Exercise training and nonsurgical management videos had significantly higher mean CFCS scores (P = 0.018). Videos by physicians (ß = 0.616; P = 0.025) were independently associated with higher JAMA scores. Advertisements were significant predictors of worse CFCS (ß = -3.978; P = 0.030), and videos by commercial sources predicted significantly lower JAMA scores (ß = -1.326; P = 0.006). CONCLUSIONS: While videos related to cervical fusion amassed a large viewership, they were poor in both quality and reliability. Videos by physicians were associated with higher reliability scores relative to other sources, whereas commercial sources and advertisements had significantly lower reliability and educational content scores. Currently, YouTube seems to be an unreliable source of information on cervical fusion for patients. LEVEL OF EVIDENCE: 4. CLINICAL RELEVANCE: The results of this study aid surgeons in counseling patients interested in cervical fusion, and suggest that publicly available videos regarding cervical fusion may not be an adequate tool for patient education at this time.

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: Current and future challenges in spine care and education - a worldwide study.

BACKGROUND: The COVID-19 pandemic has impacted spine care around the globe. Much uncertainty remains regarding the immediate and long-term future of spine care and education in this COVID-19 era. STUDY DESIGN: Cross-sectional, international study of spine surgeons. METHODS: A multi-dimensional survey was distributed to spine surgeons around the world. A total of 73 questions were asked regarding demographics, COVID-19 observations, personal impact, effect on education, adoption of telemedicine, and anticipated challenges moving forward. Multivariate analysis was performed to assess factors related to likelihood of future conference attendance, future online education, and changes in surgical indications. RESULTS: A total of 902 spine surgeons from seven global regions completed the survey. Respondents reported a mean level of overall concern of 3.7 on a scale of one to five. 84.0% reported a decrease in clinical duties, and 67.0% reported a loss in personal income. The 82.5% reported being interested in continuing a high level of online education moving forward. Respondents who personally knew someone who tested positive for COVID-19 were more likely to be unwilling to attend a medical conference 1 year from now (OR: 0.61, 95% CI: [0.39, 0.95], P = .029). The 20.0% reported they plan to pursue an increased degree of nonoperative measures prior to surgery 1 year from now, and respondents with a spouse at home (OR: 3.55, 95% CI: [1.14, 11.08], P = .029) or who spend a large percentage of their time teaching (OR: 1.45, 95% CI: [1.02, 2.07], P = .040) were more likely to adopt this practice. CONCLUSIONS: The COVID-19 pandemic has had an adverse effect on surgeon teaching, clinical volume, and personal income. In the future, surgeons with family and those personally affected by COVID-19 may be more willing to alter surgical indications and change education and conference plans. Anticipating these changes may help the spine community appropriately plan for future challenges.