Evaluating the Potential of Decision Tree Modeling to Augment Return-to-Duty Decisions Following Major Limb Injury

Advances in medical care now enable significant functional recovery after traumatic limb injuries. The return-to-duty decision-making process is highly variable and dependent on multiple factors. To retain service members (SM) post-injury, there needs to be a robust method to inform the decision-making process. The collection of outcome data and decision tree analysis has the potential to assist in the development of an efficient decision support tool. Data were combined from two previous research studies on 31 injured SMs (26 with limb salvage wearing custom dynamic ankle–foot orthoses and 5 with varying levels of lower limb amputation wearing prostheses). Forty-two factors across military, demographic, injury, and outcome measures were used to develop categorical tree models to classify return to duty after injury. The feasibility of the final pruned model was evaluated using a 10-fold cross-validation to calculate sensitivity, specificity, and misclassification rate. The overall misclassification rate for the final pruned model was 29% (9/31). The model classified participants into successful return to duty: (1) Post Concussion Symptom Scale < 20 and (2) age at time of assessment ≥34. These preliminary results suggest that decision tree modeling could be an effective approach to augmenting the return-to-duty decision-making process.