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Acute injury characteristics predict chronic neuropathic pain development after spinal cord injury.

Created on 01 Jul 2026

Authors

Kenneth A Fond, Mayra Arellano, Abel Torres-Espin, Austin Chou, Xuan Bradfield, Sara L Moncivais, J Russell Huie, Debra D Hemmerle, Anastasia V Keller, Vineeta Singh, Lisa U Pascual, Anthony M DiGiorgio, Jason F Talbott, William D Whetstone, Jonathan Z Pan, Philip R Weinstein, Sanjay S Dhall, Rajiv Saigal, Adam R Ferguson, Jacqueline C Bresnahan, Michael S Beattie, Nikos Kyritsis

Published in

Frontiers in neurology. Volume 17. Pages 1814624. Epub Jun 16, 2026.

Abstract

Neuropathic pain is one of the most common and debilitating complications following spinal cord injury (SCI), frequently surpassing motor and sensory deficits as the symptom patients most want treated. Despite advances in understanding the molecular and physiological mechanisms underlying central neuropathic pain, effective treatments remain lacking and show wide variability in efficacy. Previous reports have indicated that early intervention represents the most effective pain management strategy, underscoring the clinical importance of identifying patients at risk during the acute care phase.
We utilized the TRACK-SCI prospective clinical research database to assess neuropathic pain outcomes in all enrolled SCI patients and identify acute care variables predictive of chronic neuropathic pain development. Pain status was evaluated at 6 and 12 months post-injury. Candidate predictors were analyzed using multidimensional analytics, and a logistic regression model was constructed and validated using repeated 5-fold cross-validation.
Of 61 patients in the study cohort, 36 (59%) reported neuropathic pain in the chronic stages after SCI. Four acute care variables were identified as significant predictors of chronic neuropathic pain development: (1) the total number of systemic injuries sustained, (2) the injury severity score (ISS), (3) the lower limb total motor score, and (4) the sensory pinprick total score. The logistic regression model achieved a balanced accuracy of 74.3%, and repeated 5-fold cross-validation yielded an AUC of 0.708.
These findings highlight a crucial role of polytrauma in the development of chronic pain after SCI. The four identified predictors are parameters routinely measured in every trauma center, making the proposed model readily translatable to clinical practice. This predictive tool may enable earlier, targeted intervention for at-risk patients, addressing the clinical need for proactive pain management strategies in the acute post-SCI setting. Future work should validate this model in larger, independent cohorts and explore its utility in guiding early treatment decisions.

PMID:
42383031
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

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