Use of Regression Tree Analysis for Predicting the Functional Outcome after Traumatic Spinal Cord Injury

Predicting the long-term functional outcome after traumatic spinal cord injury (TSCI) is needed to adapt medical strategies and plan an optimized rehabilitation. This study investigates the use of regression trees for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized after TSCI. Functional outcome was quantified using the Spinal Cord Independence Measure (SCIM) collected within the first-year post-injury. Age, delay before surgery, and Injury Severity Score (ISS) were considered as continuous predictors whereas energy of injury, trauma mechanisms, neurological level of injury, injury severity, occurrence of early spasticity, urinary tract infection, pressure ulcer, and pneumonia were coded as categorical inputs. A simplified model was built using only American Spinal Injury Association Impairment Scale grade, neurological level, energy, and age as predictor and was compared to a more complex model considering all 11 predictors mentioned above. The models built using 4 and 11 predictors were found to explain 51.4% and 62.3% of the variance of the SCIM total score after validation, respectively. Severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the ISS, age, neurological level, and delay before surgery. Regression trees offer promising performances for predicting the functional outcome after a TSCI. It could help to determine the number and type of predictors leading to a prediction model of the functional outcome that can be used clinically in the future.