The Role Of Psychological Variables In Predicting Rehabilitation Outcomes After Spinal Cord Injury: An Artificial Neural Networks Study

Background: Accurate prediction of neurorehabilitation outcomes following Spinal Cord Injury (SCI) is crucial for optimizing healthcare resource allocation and improving rehabilitation strategies. Artificial neural networks (ANNs) may identify complex prognostic factors in patients with SCI. However, psychological variables’ influence on rehabilitation outcomes remains underexplored, despite their potential impact on re-covery success. Materials and Methods: A cohort of 303 patients with SCI was analyzed with an ANN model that employed 17 input variables, structured into two hidden layers and a single output node. Clinical and psychological data were integrated to predict functional outcomes, measured by the Spinal Cord Independence Measure (SCIM) at discharge. Paired Wilcoxon tests evaluated pre-post differences, and linear regression assessed correlation, with Pearson’s coefficient and Root Mean Square Error calculated. Results: Significant improvements in SCIM scores were observed (21.8 ± 15.8 at admission vs. 57.4 ± 22.5 at discharge, p < 0.001). The model assigned the highest predictive weight to SCIM at admission (10.3%), while psychological factors accounted for 36.3%, increasing to 40.9% in traumatic SCI cases. Anxiety and de-pression were the most influential psychological predictors. Correlation between predicted and actual SCIM scores was R=0.794 for the entire sample and R=0.940 for traumatic cases. Conclusion: ANN model demonstrated strong impact, especially for traumatic SCI, of psychological factors on functional outcomes. Anxiety and depression emerged as dominant negative predictors, conversely self-esteem and emotional regulation functioned as protective factors increasing functional outcomes. These findings support the integration of psychological assessments into predictive models to enhance accuracy in SCI rehabilitation outcomes.