The effect of age and injury severity on clinical prediction rules for ambulation among individuals with spinal cord injury

BACKGROUND CONTEXT: While several models for predicting independent ambulation early after traumatic spinal cord injury (SCI) based upon age and specific motor and sensory level findings have been published and validated, their accuracy, especially in individual American Spinal Injury Association [ASIA] Impairment Scale (AIS) classifications, has been questioned. Further, although age is widely used in prediction rules, its role and possible modifications have not been adequately evaluated until now. PURPOSE: To evaluate the predictive accuracy of existing clinical prediction rules for independent ambulation among individuals at spinal cord injury model systems (SCIMS) Centers as well as the effect of modifying the age parameter from a cutoff of 65 years to 50 years. STUDY DESIGN: Retrospective analysis of a longitudinal database. PATIENT SAMPLE: Adult individuals with traumatic SCI. OUTCOME MEASURES: The FIM locomotor score was used to assess independent walking ability at the 1-year follow-up. METHODS: In all, 639 patients were enrolled in the SCIMS database between 2011 and 2015, with complete neurological examination data within 15 days following the injury and a follow-up assessment with functional independence measure (FIM) at 1-year post injury. Two previously validated logistic regression models were evaluated for their ability to predict independent walking at 1-year post injury with participants in the SCIMS database. Area under the receiver operating curve (AUC) was calculated for the individual AIS categories and for different age groups. Prediction accuracy was also calculated for a new modified LR model (with cut-off age of 50). RESULTS: Overall AUC for each of the previous prediction models was found to be consistent with previous reports (0.919 and 0.904). AUCs for grouped AIS levels (A+D, B+C) were consistent with prior reports, moreover, prediction for individual AIS grades continued to reveal lower values. AUCs by different age categories showed a decline in prognostication accuracy with an increase in age, with statistically significant improvement of AUC when age-cut off was reduced to 50. CONCLUSIONS: We confirmed previous results that former prediction models achieve strong prognostic accuracy by combining AIS subgroups, yet prognostication of the separate AIS groups is less accurate. Further, prognostication of persons with AIS B+C, for whom a clinical prediction model has arguably greater clinical utility, is less accurate than those with AIS A+D. Our findings emphasize that age is an important factor in prognosticating ambulation following SCI. Prediction accuracy declines for older individuals compared with younger ones. To improve prediction of independent ambulation, the age of 50 years may be a better cutoff instead of age of 65.