Using Decision Tree Methodology to Predict Employment After Moderate to Severe Traumatic Brain Injury

Objective: To build decision tree prediction models for long-term employment outcomes of individuals after moderate to severe closed traumatic brain injury (TBI) and assess model accuracy in an independent sample. Setting: TBI Model Systems Centers. Participants: TBI Model Systems National Database participants injured between January 1997 and January 2017 with moderate to severe closed TBI. Sample sizes were 7867 (year 1 postinjury), 6783 (year 2 postinjury), and 4927 (year 5 postinjury). Design: Cross-sectional analyses using flexible classification tree methodology and validation using an independent subset of TBI Model Systems National Database participants. Main Measures: Competitive employment at 1, 2, and 5 years postinjury. Results: In the final employment prediction models, posttraumatic amnesia duration was the most important predictor of employment in each outcome year. Additional variables consistently contributing were age, preinjury education, productivity, and occupational category. Generally, individuals spending fewer days in posttraumatic amnesia, who were competitively employed preinjury, and more highly educated had better outcomes. Predictability in test data sets ranged from a C-statistic of 0.72 (year 5; confidence interval: 0.68-0.76) to 0.77 (year 1; confidence interval: 0.74-0.80). Conclusion: An easy-to-use decision tree tool was created to provide prognostic information on long-term competitive employment outcomes in individuals with moderate to severe closed TBI. Length of posttraumatic amnesia, a clinical marker of injury severity, and preinjury education and employment status were the most important predictors.