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Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.

[...]although the age-standardised incidence of TBI in 2016 was nearly 30 times greater than that of SCI (369 per 100 000 vs 13 per 100 000), the age-standardised prevalence of TBI was only about double that of SCI (759 per 100 000 vs 368 per 100 000), and the age-standardised YLD rate for TBI was lower than that of SCI (111 per 100 000 vs 130 per 100 000). [...]this study showed that the age-standardised incidence and prevalence of SCI remained stable globally from 1990 to 2016. [...]with demographic shifts, the overall pattern and morphology of these injuries are likely to have changed despite stability in the overall incidence, and such changes in distribution might vary by geographical region. [...]it would be prudent to examine how the age composition and patterns and mechanisms of injury among patients with SCI (and TBI) have changed over time and across different locations.

Although there is a strong biological rationale for early decompression of the injured spinal cord, the influence of the timing of surgical decompression for acute spinal cord injury (SCI) remains debated, with substantial variability in clinical practice. We aimed to objectively evaluate the effect of timing of decompressive surgery for acute SCI on long-term neurological outcomes. We did a pooled analysis of individual patient data derived from four independent, prospective, multicentre data sources, including data from December, 1991, to March, 2017. Three of these studies had been published; of these, only one study previously specifically analysed the effect of the timing of surgical decompression. These four datasets were selected because they were among the highest quality acute SCI datasets available and contained highly granular data. Individual patient data were obtained by request from study authors. All patients who underwent decompressive surgery for acute SCI within these datasets were included. Patients were stratified into early (<24 h after spinal injury) and late (≥24 h after spinal injury) decompression groups. Neurological outcomes were assessed by American Spinal Injury Association (ASIA), or International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), examination. The primary endpoint was change in total motor score from baseline to 1 year after spinal injury. Secondary endpoints were ASIA Impairment Scale (AIS) grade and change in upper-extremity motor, lower-extremity motor, light touch, and pin prick scores after 1 year. One-stage meta-analyses were done by hierarchical mixed-effects regression adjusting for baseline score, age, mechanism of injury, AIS grade, level of injury, and administration of methylprednisolone. Effect sizes were summarised by mean difference (MD) for sensorimotor scores and common odds ratio (cOR) for AIS grade, with corresponding 95% CIs. As a secondary analysis, change in total motor score was regressed against time to surgical decompression (h) as a continuous variable, using a restricted cubic spline with adjustment for the same covariates as in the primary analysis. We identified 1548 eligible patients from the four datasets. Outcome data at 1 year after spinal injury were available for 1031 patients (66·6%). Patients who underwent early surgical decompression (n=528) experienced greater recovery than patients who had late decompression surgery (n=1020) at 1 year after spinal injury; total motor scores improved by 23·7 points (95% CI 19·2–28·2) in the early surgery group versus 19·7 points (15·3–24·0) in the late surgery group (MD 4·0 points [1·7–6·3]; p=0·0006), light touch scores improved by 19·0 points (15·1–23·0) vs 14·8 points (11·2–18·4; MD 4·3 [1·6–7·0]; p=0·0021), and pin prick scores improved by 18·3 points (13·7–22·9) versus 14·2 points (9·8–18·6; MD 4·0 [1·5–6·6]; p=0·0020). Patients who had early decompression also had better AIS grades at 1 year after surgery, indicating less severe impairment, compared with patients who had late surgery (cOR 1·48 [95% CI 1·16–1·89]; p=0·0019). When time to surgical decompression was modelled as a continuous variable, there was a steep decline in change in total motor score with increasing time during the first 24–36 h after injury (p<0·0001); and after 36 h, change in total motor score plateaued. Surgical decompression within 24 h of acute SCI is associated with improved sensorimotor recovery. The first 24–36 h after injury appears to represent a crucial time window to achieve optimal neurological recovery with decompressive surgery following acute SCI. None.

Degenerative cervical myelopathy (DCM) is the leading cause of spinal cord dysfunction in adults worldwide. DCM encompasses various acquired (age-related) and congenital pathologies related to degeneration of the cervical spinal column, including hypertrophy and/or calcification of the ligaments, intervertebral discs and osseous tissues. These pathologies narrow the spinal canal, leading to chronic spinal cord compression and disability. Owing to the ageing population, rates of DCM are increasing. Expeditious diagnosis and treatment of DCM are needed to avoid permanent disability. Over the past 10 years, advances in basic science and in translational and clinical research have improved our understanding of the pathophysiology of DCM and helped delineate evidence-based practices for diagnosis and treatment. Surgical decompression is recommended for moderate and severe DCM; the best strategy for mild myelopathy remains unclear. Next-generation quantitative microstructural MRI and neurophysiological recordings promise to enable quantification of spinal cord tissue damage and help predict clinical outcomes. Here, we provide a comprehensive, evidence-based review of DCM, including its definition, epidemiology, pathophysiology, clinical presentation, diagnosis and differential diagnosis, and non-operative and operative management. With this Review, we aim to equip physicians across broad disciplines with the knowledge necessary to make a timely diagnosis of DCM, recognize the clinical features that influence management and identify when urgent surgical intervention is warranted.Degenerative cervical myelopathy is the leading cause of spinal cord dysfunction in adults worldwide. In this Review, the authors provide a comprehensive pathophysiological and clinical overview of the condition to equip physicians across broad disciplines with the knowledge needed for its diagnosis and management.

Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.

Abstract BACKGROUND Surgical decompression for degenerative cervical myelopathy (DCM) is one of the mainstays of treatment, with generally positive outcomes. However, some patients who undergo surgery for DCM continue to show functional decline. OBJECTIVE To use machine learning (ML) algorithms to determine predictors of worsening functional status after surgical intervention for DCM. METHODS This is a retrospective analysis of prospectively collected data. A total of 757 patients enrolled in 2 prospective AO Spine clinical studies, who underwent surgical decompression for DCM, were analyzed. The modified Japanese Orthopedic Association (mJOA) score, a marker of functional status, was obtained before and 1 yr postsurgery. The primary outcome measure was the dichotomized change in mJOA at 1 yr according to whether it was negative (worse functional status) or non-negative. After applying an 80:20 training-testing split of the dataset, we trained, optimized, and tested multiple ML algorithms to evaluate algorithm performance and determine predictors of worse mJOA at 1 yr. RESULTS The highest-performing ML algorithm was a polynomial support vector machine. This model showed good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.834 (accuracy: 74.3%, sensitivity: 88.2%, specificity: 72.4%). Important predictors of functional decline at 1 yr included initial mJOA, male gender, duration of myelopathy, and the presence of comorbidities. CONCLUSION The reasons for worse mJOA are frequently multifactorial (eg, adjacent segment degeneration, tandem lumbar stenosis, ongoing neuroinflammatory processes in the cord). This study successfully used ML to predict worse functional status after surgery for DCM and to determine associated predictors. Graphical Abstract Graphical Abstract

Spinal cord injuries (SCIs) are associated with tremendous physical, social, and financial costs for millions of individuals and families worldwide. Rapid delivery of specialized medical and surgical care has reduced mortality; however, long‐term functional recovery remains limited. Cell‐based therapies represent an exciting neuroprotective and neuroregenerative strategy for SCI. This article summarizes the most promising preclinical and clinical cell approaches to date including transplantation of mesenchymal stem cells, neural stem cells, oligodendrocyte progenitor cells, Schwann cells, and olfactory ensheathing cells, as well as strategies to activate endogenous multipotent cell pools. Throughout, we emphasize the fundamental biology of cell‐based therapies, critical features in the pathophysiology of spinal cord injury, and the strengths and limitations of each approach. We also highlight salient completed and ongoing clinical trials worldwide and the bidirectional translation of their findings. We then provide an overview of key adjunct strategies such as trophic factor support to optimize graft survival and differentiation, engineered biomaterials to provide a support scaffold, electrical fields to stimulate migration, and novel approaches to degrade the glial scar. We also discuss important considerations when initiating a clinical trial for a cell therapy such as the logistics of clinical‐grade cell line scale‐up, cell storage and transportation, and the delivery of cells into humans. We conclude with an outlook on the future of cell‐based treatments for SCI and opportunities for interdisciplinary collaboration in the field.

Study Design: Narrative review. Objective: To summarize relevant studies regarding the utilization of intraoperative neurophysiological monitoring (IONM) techniques in spine surgery implemented in recent years. Methods: A literature search of the Medline database was performed. Relevant studies from all evidence levels have been included. Titles, abstracts, and reference lists of key articles were included. Results: Multimodal intraoperative neurophysiological monitoring (MIONM) has the advantage of compensating for the limitations of each individual technique and seems to be effective and accurate for detecting perioperative neurological injury during spine surgery. Conclusion: Although there are no prospective studies validating the efficacy of IONM, there is a growing body of evidence supporting its use during spinal surgery. However, the lack of validated protocols to manage intraoperative alerts highlights a critical knowledge gap. Future investigation should focus on developing treatment methodology, validating practice protocols, and synthesizing clinical guidelines.

Abstract BACKGROUND There is controversy over the optimal treatment strategy for patients with mild degenerative cervical myelopathy (DCM). OBJECTIVE To evaluate the degree of impairment in baseline quality of life as compared to population norms, as well as functional, disability, and quality of life outcomes following surgery in a prospective cohort of mild DCM patients undergoing surgical decompression. METHODS We identified patients with mild DCM (modified Japanese Orthopaedic Association [mJOA] 15 to 17) enrolled in the prospective, multicenter AOSpine CSM-NA or CSM-I trials. Baseline quality of life Short Form-36 version 2 (SF-36v2) was compared to population norms by the standardized mean difference (SMD). Outcomes, including functional status (mJOA, Nurick grade), disability (NDI [Neck Disability Index]), and quality of life (SF-36v2), were evaluated at baseline and 6 mo, 1 yr, and 2 yr after surgery. Postoperative complications within 30 d of surgery were monitored. RESULTS One hundred ninety-three patients met eligibility criteria. Mean age was 52.4 yr. There were 67 females (34.7%). Patients had significant impairment in all domains of the SF-36v2 compared to population norms, greatest for Social Functioning (SMD –2.33), Physical Functioning (SMD –2.31), and Mental Health (SMD –2.30). A significant improvement in mean score from baseline to 2-yr follow-up was observed for all major outcome measures, including mJOA (0.87, P < .01), Nurick grade (–1.13, P < .01), NDI (–12.97, P < .01), and SF-36v2 Physical Component Summary (PCS) (5.75, P < .01) and Mental Component Summary (MCS) (6.93, P < .01). The rate of complication was low. CONCLUSION Mild DCM is associated with significant impairment in quality of life. Surgery results in significant gains in functional status, level of disability, and quality of life.