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Traumatic spinal cord injury (SCI) disrupts the spinal cord vasculature resulting in ischemia, amplification of the secondary injury cascade and exacerbation of neural tissue loss. Restoring functional integrity of the microvasculature to prevent neural loss and to promote neural repair is an important challenge and opportunity in SCI research. Herein, we summarize the course of vascular injury and repair following SCI and give a comprehensive overview of current experimental therapeutic approaches targeting spinal cord microvasculature to diminish ischemia and thereby facilitate neural repair and regeneration. A systematic review of the published literature on therapeutic approaches to promote vascular repair after experimental SCI was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards. The MEDLINE databases PubMed, Embase, and OVID MEDLINE were searched using the keywords “spinal cord injury,” “angiogenesis,” “angiogenesis inducing agents,” “tissue engineering,” and “rodent subjects.” A total of 111 studies were identified through the search. Five main therapeutic approaches to diminish hypoxia-ischemia and promote vascular repair were identified as (1) the application of angiogenic factors, (2) genetic engineering, (3) physical stimulation, (4) cell transplantation, and (5) biomaterials carrying various factor delivery. There are different therapeutic approaches with the potential to diminish hypoxia-ischemia and promote vascular repair after experimental SCI. Of note, combinatorial approaches using implanted biomaterials and angiogenic factor delivery appear promising for clinical translation.
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Objective Mechanism of injury is a largely understudied descriptor of acute traumatic spinal cord injury (tSCI). This study sought to compare the impact of high-energy and low-energy mechanisms of injury in neurological outcomes of cervical sensorimotor complete tSCI.
Methods Patients with tSCI were identified in 4 prospective, multicenter clinical trials and registries. American Spinal Injury Association Impairment Scale (AIS) grade was assessed ≤ 72 hours postinjury and followed up between 12 to 52 weeks. Patients were included if they had a cervical and sensorimotor complete (AIS–A) injury at baseline. Study outcomes were change in AIS grade and lower extremity motor, upper extremity motor, and total motor scores. Propensity score matching between high-energy mechanisms of injury (HEMI; e.g. , motor vehicle collisions) and low-energy mechanisms of injury (LEMI; e.g. , falls) groups was performed. Adjusted groups were compared with paired t-tests and McNemar test.
Results Of 667 patients eligible for inclusion, 523 experienced HEMI (78.4%). HEMI patients were younger, had lower body mass index, more associated fractures or dislocations, and lower baseline lower extremity motor scores. After propensity score matching of these baseline variables, 118 pairs were matched. HEMI patients had a significantly worse motor recovery from baseline to follow-up based on their diminished change in upper extremity motor scores and total motor scores.
Conclusion Cervical sensorimotor complete tSCIs from HEMI were associated with significantly lower motor recovery compared to LEMI patients. Our findings suggest that mechanism of injury should be considered in modelling prognosis and in understanding the heterogeneity of outcomes after acute tSCI.
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Anne L. Versteeg, Lior M. Elkaim, Arjun Sahgal, Laurence D. Rhines, Daniel M. Sciubba, James M. Schuster, Michael G. Fehlings, Aron Lazary, Michelle J. Clarke, Paul M. Arnold, Chetan Bettegowda, Stefano Boriani, Ziya L. Gokaslan, Charles G. Fisher, Michael H. Weber, AO Spine Knowledge Forum Tumor
Neurospine 2022;19(1):43-50. Published online January 30, 2022
Objective Patients presenting with neurological deficit secondary to metastatic epidural spinal cord compression (MESCC) are often treated with surgery in combination with high-dose corticosteroids. Despite steroids being commonly used, the evidence regarding the effect of corticosteroids on patient outcomes is limited. The objective of this study was to describe the effect of corticosteroid use on preoperative neurological function in patients with MESCC.
Methods Patients who underwent surgery between August 2013 and February 2017 for the treatment of spinal metastases and received steroids to prevent neurologic deficits were included. Data regarding demographics, diagnosis, treatment, neurological function, adverse events, health-related quality of life, and survival were extracted from an international multicenter prospective cohort.
Results A total of 30 patients treated surgically and receiving steroids at baseline were identified. Patients had a mean age of 58.2 years (standard deviation, 11.2 years) at time of surgery. Preoperatively, 50% of the patients experienced deterioration of neurological function, while in 30% neurological function was stable and 20% improved in neurological function. Lengthier steroid use did not correlate with improved or stabilized neurological function. Postoperative adverse events were observed in 18 patients (60%). Patients that stabilized or improved neurologically after steroid use showed a trend towards improved survival at 3- and 24-month postsurgery.
Conclusion This study described the effect of steroids on preoperative neurological function in patients with MESCC. Stabilization or improvement of preoperative neurological function occurred in 50% of the patients.
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Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.
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The assessment, diagnosis, operative and nonoperative management of degenerative cervical myelopathy (DCM) have evolved rapidly over the last 20 years. A clearer understanding of the pathobiology of DCM has led to attempts to develop objective measurements of the severity of myelopathy, including technology such as multiparametric magnetic resonance imaging, biomarkers, and ancillary clinical testing. New pharmacological treatments have the potential to alter the course of surgical outcomes, and greater innovation in surgical techniques have made surgery safer, more effective and less invasive. Future developments for the treatment of DCM will seek to improve the diagnostic accuracy of imaging, improve the objectivity of clinical assessment, and increase the use of surgical technology to ensure the best outcome is achieved for each individual patient.
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