Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study.

BACKGROUND: Degenerative cervical myelopathy (DCM), a progressive spinal cord injury caused by spinal cord compression from degenerative pathology, often presents with neck pain, sensorimotor dysfunction in the upper or lower limbs, gait disturbance, and bladder or bowel dysfunction. Its symptomatology is very heterogeneous, making early detection as well as the measurement or understanding of the underlying factors and their consequences challenging. Increasingly, evidence suggests that DCM may consist of subgroups of the disease, which are yet to be defined. OBJECTIVE: This study aimed to explore whether machine learning can identify clinically meaningful groups of patients based solely on clinical features. METHODS: A survey was conducted wherein participants were asked to specify the clinical features they had experienced, their principal presenting complaint, and time to diagnosis as well as demographic information, including disease severity, age, and sex. K-means clustering was used to divide respondents into clusters according to their clinical features using the Euclidean distance measure and the Hartigan-Wong algorithm. The clinical significance of groups was subsequently explored by comparing their time to presentation, time with disease severity, and other demographics. RESULTS: After a review of both ancillary and cluster data, it was determined by consensus that the optimal number of DCM response groups was 3. In Cluster 1, there were 40 respondents, and the ratio of male to female participants was 13:21. In Cluster 2, there were 92 respondents, with a male to female participant ratio of 27:65. Cluster 3 had 57 respondents, with a male to female participant ratio of 9:48. A total of 6 people did not report biological sex in Cluster 1. The mean age in this Cluster was 56.2 (SD 10.5) years; in Cluster 2, it was 54.7 (SD 9.63) years; and in Cluster 3, it was 51.8 (SD 8.4) years. Patients across clusters significantly differed in the total number of clinical features reported, with more clinical features in Cluster 3 and the least clinical features in Cluster 1 (Kruskal-Wallis rank sum test: χ22=159.46; P<.001). There was no relationship between the pattern of clinical features and severity. There were also no differences between clusters regarding time since diagnosis and time with DCM. CONCLUSIONS: Using machine learning and patient-reported experience, 3 groups of patients with DCM were defined, which were different in the number of clinical features but not in the severity of DCM or time with DCM. Although a clearer biological basis for the clusters may have been missed, the findings are consistent with the emerging observation that DCM is a heterogeneous disease, difficult to diagnose or stratify. There is a place for machine learning methods to efficiently assist with pattern recognition. However, the challenge lies in creating quality data sets necessary to derive benefit from such approaches.

Human spinal cord tissue is an underutilised resource in degenerative cervical myelopathy: findings from a systematic review of human autopsies.

STUDY DESIGN: Systematic review. BACKGROUND: Although degenerative cervical myelopathy (DCM) is the most prevalent spinal cord condition worldwide, the pathophysiology remains poorly understood. Our objective was to evaluate existing histological findings of DCM on cadaveric human spinal cord tissue and explore their consistency with animal models. METHODS: MEDLINE and Embase were systematically searched (CRD42021281462) for primary research reporting on histological findings of DCM in human cadaveric spinal cord tissue. Data was extracted using a piloted proforma. Risk of bias was assessed using Joanna Briggs Institute critical appraisal tools. Findings were compared to a systematic review of animal models (Ahkter et al. 2020 Front Neurosci 14). RESULTS: The search yielded 4127 unique records. After abstract and full-text screening, 19 were included in the final analysis, reporting on 150 autopsies (71% male) with an average age at death of 67.3 years. All findings were based on haematoxylin and eosin (H&E) staining. The most commonly reported grey matter findings included neuronal loss and cavity formation. The most commonly reported white matter finding was demyelination. Axon loss, gliosis, necrosis and Schwann cell proliferation were also reported. Findings were consistent amongst cervical spondylotic myelopathy and ossification of the posterior longitudinal ligament. Cavitation was notably more prevalent in human autopsies compared to animal models. CONCLUSION: Few human spinal cord tissue studies have been performed. Neuronal loss, demyelination and cavitation were common findings. Investigating the biological basis of DCM is a critical research priority. Human spinal cord specimen may be an underutilised but complimentary approach.

Improving Assessment of Disease Severity and Strategies for Monitoring Progression in Degenerative Cervical Myelopathy [AO Spine RECODE-DCM Research Priority Number 4].

STUDY DESIGN: Narrative Review. OBJECTIVE: To (i) discuss why assessment and monitoring of disease progression is critical in Degenerative cervical myelopathy (DCM); (ii) outline the important features of an ideal assessment tool and (iii) discuss current and novel strategies for detecting subtle deterioration in DCM. METHODS: Literature review. RESULTS: Degenerative cervical myelopathy is an overarching term used to describe progressive injury to the cervical spinal cord by age-related changes of the spinal axis. Based on a study by Smith et al (2020), the prevalence of DCM is approximately 2.3% and is expected to rise as the global population ages. Given the global impact of this disease, it is essential to address important knowledge gaps and prioritize areas for future investigation. As part of the AO Spine RECODE-DCM (Research Objectives and Common Data Elements for Degenerative Cervical Myelopathy) project, a priority setting partnership was initiated to increase research efficiency by identifying the top ten research priorities for DCM. One of the top ten priorities for future DCM research was: What assessment tools can be used to evaluate functional impairment, disability and quality of life in people with DCM? What instruments, tools or methods can be used or developed to monitor people with DCM for disease progression or improvement either before or after surgical treatment? CONCLUSIONS: With the increasing prevalence of DCM, effective surveillance of this population will require both the implementation of a monitoring framework as well as the development of new assessment tools.