The use of regression tree analysis for predicting the functional outcome following traumatic spinal cord injury.

Predicting the long-term functional outcome following traumatic spinal cord injury is needed to adapt medical strategies and to plan an optimized rehabilitation. This study investigates the use of regression tree for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized following traumatic spinal cord injury. Functional outcome was quantified using the Spinal Cord Independence Measure collected within the first-year post injury. Age, delay prior to surgery and Injury Severity Score were considered as continuous predictors while 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 injury severity, 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 Spinal Cord Independence Measure total score after validation, respectively. The severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the Injury Severity Score, age, neurological level and delay prior to surgery. Regression trees offer promising performances for predicting the functional outcome after a traumatic spinal cord injury. 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.