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.

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.

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.

How Does Resorption Differ Among Single-Level and Multilevel Lumbar Disc Herniations? A Prospective Multi-Imaging and Clinical Phenotype Study.

STUDY DESIGN: Prospective, case series. OBJECTIVE: To identify and characterize any differences in specific patient factors, MRI findings, features of spontaneous disc resorption, and outcomes between patients with single-level and multilevel LDH. BACKGROUND: Lumbar disc herniation (LDH) is one of the most common spinal pathologies worldwide. Though many cases of LDH resolve by spontaneous resorption, the mechanism underlying this "self-healing" phenomenon remains poorly understood, particularly in the context of multilevel herniations. METHODS: A one-year prospective study was conducted of patients presenting with acute symptomatic LDH between 2017 and 2019. Baseline demographics, herniation characteristics, and MRI phenotypes were recorded before treatment, which consisted of gabapentin, acupuncture, and the avoidance of inflammatory-modulating medications. MRIs were performed approximately every three months after the initial evaluation to determine any differences between patients with single-level and multilevel LDH. RESULTS: Ninety patients were included, 17 demonstrated multilevel LDH. Body mass index was higher among patients with multilevel LDH ( P <0.001). Patients with multilevel LDH were more likely to exhibit L3/L4 inferior endplate defects ( P =0.001), L4/L5 superior endplate defects ( P =0.012), and L4/L5 inferior endplate defects ( P =0.020) on MRI. No other differences in MRI phenotypes ( e.g. Modic changes, osteophytes, etc .) existed between groups. Resorption rate and time to resolution did not differ between those with single-level and multilevel LDH. CONCLUSIONS: Resorption rates were similar between single-level and multilevel LDH at various time points throughout one prospective assessment, providing insights that disc healing may have unique programmed signatures. Compared with those with single-level LDH, patients with multilevel herniations were more likely to have a higher BMI, lesser initial axial and sagittal disc measurements, and endplate defects at specific lumbar levels. In addition, our findings support the use of conservative management in patients with LDH, regardless of the number of levels affected. LEVEL OF EVIDENCE: Level 3.

Prediction of lumbar disc herniation resorption in symptomatic patients: a prospective, multi-imaging and clinical phenotype study.

BACKGROUND CONTEXT: Symptomatic lumbar disc herniations (LDH) are very common. LDH resorption may occur by a "self-healing" process, however this phenomenon remains poorly understood. By most guidelines, if LDH remains symptomatic after 3 months and conservative management fails, surgical intervention may be an option. PURPOSE: The following prospective study aimed to identify determinants that may predict early versus late LDH resorption. STUDY DESIGN/SETTING: Prospective study with patients recruited at a single center. PATIENT SAMPLE: Ninety-three consecutive patients diagnosed with acute symptomatic LDH were included in this study (n=23 early resorption and n=67 late resorption groups) with a mean age of 48.7±11.9 years. OUTCOMES MEASURE: Baseline assessment of patient demographics (eg, smoking status, height, weight, etc.), herniation characteristics (eg, the initial level of herniation, the direction of herniation, prevalence of multiple herniations, etc.) and MRI phenotypes (eg, Modic changes, end plate abnormalities, disc degeneration, vertebral body dimensions, etc.) were collected for further analysis. Lumbar MRIs were performed approximately every 3 months for 1 year from time of enrollment to assess disc integrity. METHODS: All patients were managed similarly. LDH resorption was classified as early (<3 months) or late (>3 months). A prediction model of pretreatment factors was constructed. RESULTS: No significant differences were noted between groups at any time-point (p>.05). Patients in the early resorption group experienced greater percent reduction of disc herniation between MRI-0-MRI-1 (p=.043), reduction of herniation size for total study duration (p=.007), and percent resorption per day compared to the late resorption group (p<.001). Based on multivariate modeling, greater L4 posterior vertebral height (coeff:14.58), greater sacral slope (coeff:0.12), and greater herniated volume (coeff:0.013) at baseline were found to be most predictive of early resorption (p<.05). CONCLUSIONS: This is the first comprehensive imaging and clinical phenotypic prospective study, to our knowledge, that has identified distinct determinants for early LDH resorption. Early resorption can occur in 24.7% of LDH patients. We developed a prediction model for early resorption which demonstrated great overall performance according to pretreatment measures of herniation size, L4 posterior body height, and sacral slope. A risk profile is proposed which may aid clinical decision-making and managing patient expectations.

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.