Unsupervised Data Driven Clustering of the Neurological Assessments of People With Traumatic SCI Focusing on Sensorimotor Complete Injuries

Abstract

Background:

Spinal cord injury (SCI) leads to lifelong disability with highly variable neurological recovery, complicating prognostication and conceptualization of clinical trials. The American Spinal Injury Association Impairment Scale (AIS) is widely used to classify injury severity. Although AIS A injuries are considered sensorimotor complete, they show substantial heterogeneity in residual function and recovery. Data-driven approaches offer an opportunity to uncover latent subgroups beyond conventional classifications. We evaluate whether unsupervised, data-driven clustering can identify distinct subgroups within patients with traumatic SCI and characterize neurological patterns in sensorimotor complete SCI.

Methods:

We applied an unsupervised clustering model to International Standards of Neurological Classification of Spinal Cord Injury (ISNCSCI) examination scores from the European Multicenter Study about Spinal Cord Injury dataset (3165 patients), to derive neurological groupings independent of predefined ISNCSCI classifications. Clusters were derived from the full cohort, followed by focused analyses of individuals classified as AIS A at their first documented assessment. External reproducibility was evaluated using data from the Sygen clinical trial.

Results:

Six distinct clusters were identified. Patients graded as AIS A were represented in 5 clusters, which differed markedly in injury level (paraplegic vs tetraplegic) and indicators of recovery potential, including neurological sparing, upper and lower extremity motor scores, and AIS conversion rates. These patterns were consistently reproduced in the Sygen cohort.

Conclusions:

Proposed framework complements conventional AIS grading by revealing distinct neurological conditions related to the variability among patients with baseline sensorimotor complete injuries. Proposed data-driven framework enables more comprehensive prognostic assessments and improves patient stratification in clinical trials.