Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review

Epidemiological Characteristics of Traumatic Spinal Cord Injury in Saudi Arabia: A Systematic Review

Traumatic spinal cord injury (TSCI) is a severe condition with high mortality and disability rates. Understanding the regional TSCI epidemiology may facilitate the development of targeted preventive initiatives and the optimization of resource allocation. The primary goal of this systematic review was to gather and analyze the existing literature on the frequency and characteristics of TSCI in Saudi Arabia. A literature search of PubMed, Web of Science, and Google Scholar was conducted in January 2024 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Observational studies reporting TSCI epidemiology in Saudi Arabia between 2010 and 2022 were included. Data on demographics, mechanisms, levels/severity, and outcomes were extracted. Methodological quality was assessed using the Newcastle-Ottawa Scale. Nine studies involving 2,356 TSCI cases were analyzed. Most patients were young males. Road traffic accidents were shown to be the predominant cause, accounting for 56.5-90.8% of cases. Thoracic (28.7-48.3%) and cervical (26.6-39%) levels were the most common. The extent of neurological deficits showed significant variation throughout the studies. This review provides a baseline understanding of TSCI epidemiology in Saudi Arabia but highlights critical gaps that future research should address. The review emphasizes the need for evidence-based interventions targeting road safety and falls, standardized cervical spine evaluation and management, and the use of validated metrics to optimize patient outcomes. Large-scale population-based studies with standardized methodologies are necessary to fully understand TSCI epidemiology, prognosis, and long-term disability burden in Saudi Arabia, leading to better prevention strategies and improved patient outcomes.

A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

Background

Conducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.

Purpose

We sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.

Study design

Retrospective analysis using prospectively collected data from a large national multicenter SCI registry.

Methods

A method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.

Results

Data on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.

Conclusion

Utilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.