Unbiased Recursive Partitioning to Stratify Patients with Acute Traumatic Spinal Cord Injuries: External Validity in an Observational Cohort Study

Clinical trials of novel therapies for acute spinal cord injury (SCI) are challenging because variability in spontaneous neurologic recovery can make discerning actual treatment effects difficult. Unbiased Recursive Partitioning regression with Conditional Inference Trees (URP-CTREE) is a novel approach developed through analyses of a large European SCI database (European Multicenter Study about Spinal Cord Injury). URP-CTREE uses early neurologic impairment to predict achieved motor recovery, with potential to optimize clinical trial design by optimizing patient stratification and decreasing sample sizes. We performed external validation to determine how well a previously reported URP-CTREE model stratified patients into distinct homogeneous subgroups and predicted subsequent neurologic recovery in an independent cohort. We included patients with acute cervical SCI level C4-C6 from a prospective registry at a quaternary care center from 2004-2018 ( n  = 101) and applied the URP-CTREE model and evaluated Upper Extremity Motor Score (UEMS) recovery, considered correctly predicted when final UEMS scores were within a pre-specified threshold of 9 points from median; sensitivity analyses evaluated the effect of timing of baseline neurological examination. We included 101 patients, whose mean times from injury baseline and follow-up examinations were 6.1 days (standard deviation [SD] 17) and 235.0 days (SD 71), respectively. Median UEMS recovery was 7 points (interquartile range 2-12). One of the predictor variables was not statistically significant in our sample; one group did not fit progressively improving UEMS scores, and three of five groups had medians that were not significantly different from adjacent groups. Overall accuracy was 75%, but varied from 82% among participants whose examinations occurred at <12 h, to 64% at 12-24 h, and 58% at >24 h. A previous URP-CTREE model had limited ability to stratify an independent into homogeneous subgroups. Overall accuracy was promising, but may be sensitive to timing of baseline neurological examinations. Further evaluation of external validity in incomplete injuries, influence of timing of baseline examinations, and investigation of additional stratification strategies is warranted.