Prediction of Bladder Outcomes after Traumatic Spinal Cord Injury: A Longitudinal Cohort Study

Neurogenic bladder dysfunction represents one of the most common and devastating sequelae of traumatic spinal cord injury (SCI). As early prediction of bladder outcomes is essential to counsel patients and to plan neurourological management, we aimed to develop and validate a model to predict urinary continence and complete bladder emptying 1 y after traumatic SCI.

Using multivariate logistic regression analysis from the data of 1,250 patients with traumatic SCI included in the European Multicenter Spinal Cord Injury study, we developed two prediction models of urinary continence and complete bladder emptying 1 y after traumatic SCI and performed an external validation in 111 patients. As predictors, we evaluated age, gender, and all variables of the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) and of the Spinal Cord Independence Measure (SCIM). Urinary continence and complete bladder emptying 1 y after SCI were assessed through item 6 of SCIM. The full model relies on lower extremity motor score (LEMS), light-touch sensation in the S3 dermatome of ISNCSI, and SCIM subscale respiration and sphincter management: the area under the receiver operating characteristics curve (aROC) was 0.936 (95% confidence interval [CI]: 0.922-0.951). The simplified model is based on LEMS only: the aROC was 0.912 (95% CI: 0.895-0.930). External validation of the full and simplified models confirmed the excellent predictive power: the aROCs were 0.965 (95% CI: 0.934-0.996) and 0.972 (95% CI 0.943-0.999), respectively. This study is limited by the substantial number of patients with a missing 1-y outcome and by differences between derivation and validation cohort.

Our study provides two simple and reliable models to predict urinary continence and complete bladder emptying 1 y after traumatic SCI. Early prediction of bladder function might optimize counselling and patient-tailored rehabilitative interventions and improve patient stratification in future clinical trials.