Beyond Clinical Predictors: Determining the Impact of Psychosocial Factors in Chronic Pain After Spinal Cord Injury

Although chronic pain is common after traumatic spinal cord injury (SCI) and prognostic models have traditionally prioritized clinical injury characteristics, the relative contribution of sociodemographic and psychological factors remains undercharacterized.

Secondary analysis of a national longitudinal cohort from United States inpatient rehabilitation centers participating in the Spinal Cord Injury Model Systems. Adults with traumatic SCI (N=1303) completed Spinal Cord Injury Model Systems Form 1 and a 1-year follow-up Form 2 interview. Pain intensity at 1 year was assessed using the Numeric Rating Scale (0-10) for the prior 4 weeks (mean 4.3 [SD, 2.8]; 85.4% reported pain). Hierarchical linear and quantile regression models were used to evaluate the incremental predictive value of biopsychosocial (BPS) domains, alongside Elastic Net regression and Boruta feature selection to identify robust predictors.

Sequential addition of BPS domains improved model fit across hierarchical regressions, regardless of entry order (full model R_Adjusted²≈12%). Social/demographic variables accounted for the largest share of predictive signal and were consistently prioritized over clinical factors and psychological measures. Elastic Net regression selected 43 predictors across domains, whereas Boruta confirmed 27. Six predictors were consistently identified across all methods: median household income, race, education, tobacco use, primary payer, and depression severity.

Sociodemographic factors accounted for the most predictive value for chronic pain after SCI, often outperforming traditional clinical and psychological measures. Converging results across methods support the BPS model and highlight potential targets for screening and intervention. Prospective studies are needed to clarify causal pathways and inform pain prevention efforts.