Ulnar nerve integrity predicts 1-year outcome in cervical spinal cord injury

Ulnar compound muscle action potentials predict hand muscle strength 1 year after cervical spinal cord injury: A retrospective analysis

BACKGROUND

Lower motor neuron (LMN) dysfunction caused by anterior horn cell damage in the ventral gray matter during spinal cord injury (SCI) may impact long-term prognosis.

OBJECTIVES

To determine the influence of the 3-month ulnar compound muscle action potentials (CMAP; representative of C8-T1 spinal segmental LMN integrity) on hand muscle strength and function, 12 months following SCI.

METHODS

We completed retrospective analyses of the European Multicenter Study about SCI (EMSCI) database. Included participants had traumatic SCI (motor complete or incomplete), initial neurological level of injury C1-C8, and ulnar CMAP from the abductor digiti minimi in at least one limb, 3 months after injury. We trichotomized 3-month ulnar CMAP into absent (CMAP = 0.0 mV), reduced (CMAP <6.0 mV), and normal (CMAP ≥6.0 mV), and constructed logistical regression models to predict 12-month C8 and T1 motor scores, dichotomized into poor (≤3) and functional (>3). We explored relationships between trichotomized 3-month ulnar CMAP and 12-month functional Graded Redefined Assessment of Strength, Sensation and Prehension (GRASSP) and Spinal Cord Independence Measure (SCIM) upper limb sub-scales, using non-parametric statistics.

RESULTS

Data from 318 participants (253 males), 46.8 years old (SD 18.4), resulted in CMAP and corresponding motor scores in 629 limbs. Adjusted logistical regression models were significant for C8 and T1 motor scores, with absent (C8 36.6, 95 % CI 12.9-133; T1 38.7, 95 % CI 11.2-24) and reduced (C8 11.0, 95 % CI 6.7-18.4; T1 7.93, 95 % CI 5.2-12.3) CMAP, predictive of poor 12-month motor scores. 12-month GRASSP (n = 30) and SCIM scores were significantly higher in those with normal 3-month ulnar CMAPs than absent and reduced.

CONCLUSION

There is a 7 to 38-fold higher likelihood that SCI individuals with reduced or absent 3-month ulnar CMAP will demonstrate poor hand motor scores at 12 months. This aligns with significantly worse GRASSP and SCIM functional scores. Our findings justify adding LMN health measures in prognostic modeling after SCI.

Corticospinal Tract Sparing in Cervical Spinal Cord Injury

Disruptions in the brain's connections to the hands resulting from a cervical spinal cord injury (cSCI) can lead to severe and persistent functional impairments. The integrity of these connections is an important predictor of upper extremity recovery in stroke and may similarly act as a biomarker in cSCI. In this perspective article, we review recent findings from a large cohort of individuals with cSCI, demonstrating the predictive value of corticospinal tract (CST) integrity in cSCI-CST sparing. This research underscores that, akin to stroke, the integrity of brain-to-hand connections is crucial for predicting upper extremity recovery following cSCI. We address the limitations of commonly used metrics, such as sacral sparing and the concept of central cord syndrome. Furthermore, we offer insights on emerging metrics, such as tissue bridges, emphasizing their potential in assessing the integrity of brain connections to the spinal cord.

Data-driven prediction of spinal cord injury recovery: An exploration of current status and future perspectives

Spinal Cord Injury (SCI) presents a significant challenge in rehabilitation medicine, with recovery outcomes varying widely among individuals. Machine learning (ML) is a promising approach to enhance the prediction of recovery trajectories, but its integration into clinical practice requires a thorough understanding of its efficacy and applicability. We systematically reviewed the current literature on data-driven models of SCI recovery prediction. The included studies were evaluated based on a range of criteria assessing the approach, implementation, input data preferences, and the clinical outcomes aimed to forecast. We observe a tendency to utilize routinely acquired data, such as International Standards for Neurological Classification of SCI (ISNCSCI), imaging, and demographics, for the prediction of functional outcomes derived from the Spinal Cord Independence Measure (SCIM) III and Functional Independence Measure (FIM) scores with a focus on motor ability. Although there has been an increasing interest in data-driven studies over time, traditional machine learning architectures, such as linear regression and tree-based approaches, remained the overwhelmingly popular choices for implementation. This implies ample opportunities for exploring architectures addressing the challenges of predicting SCI recovery, including techniques for learning from limited longitudinal data, improving generalizability, and enhancing reproducibility. We conclude with a perspective, highlighting possible future directions for data-driven SCI recovery prediction and drawing parallels to other application fields in terms of diverse data types (imaging, tabular, sequential, multimodal), data challenges (limited, missing, longitudinal data), and algorithmic needs (causal inference, robustness).

Improving Diagnostic Workup Following Traumatic Spinal Cord Injury: Advances in Biomarkers

PURPOSE OF REVIEW

Traumatic spinal cord injury (SCI) is a life-changing event with drastic implications for patients due to sensorimotor impairment and autonomous dysfunction. Current clinical evaluations focus on the assessment of injury level and severity using standardized neurological examinations. However, they fail to predict individual trajectories of recovery, which highlights the need for the development of advanced diagnostics. This narrative review identifies recent advances in the search of clinically relevant biomarkers in the field of SCI.

RECENT FINDINGS

Advanced neuroimaging and molecular biomarkers sensitive to the disease processes initiated by the SCI have been identified. These biomarkers range from advanced neuroimaging techniques, neurophysiological readouts, and molecular biomarkers identifying the concentrations of several proteins in blood and CSF samples. Some of these biomarkers improve current prediction models based on clinical readouts. Validation with larger patient cohorts is warranted. Several biomarkers have been identified-ranging from imaging to molecular markers-that could serve as advanced diagnostic and hence supplement current clinical assessments.