Biomechanical differences between cases with chronic exertional compartment syndrome and asymptomatic controls during walking and marching gait

Academic Department of Military Rehabilitation (ADMR): avoiding the pitfalls of 'the Walker Dip'

A key research theme identified during the 2021 Strategic Delivery Plan (SDP) for Defence Medical Services (DMS) Research was preventing and treating musculoskeletal injury (MSKI). MSKI is a significant burden to military populations globally, reducing both operational strength and force readiness. It is therefore essential that research is conducted to gain a greater understanding of the epidemiology, aetiology and risk factors associated with MSKI to develop targeted prevention strategies and rehabilitation interventions. The Academic Department of Military Rehabilitation (ADMR) and the recently established MSKI research-theme working group must use a combination of balance and flexibility when coordinating research priorities to ensure they align and reflect both higher level DMS and UK Defence Rehabilitation practitioner-driven priorities. This article describes the response ADMR have taken to meet and align with the requirements of the 2021 SDP.

Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies

Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms. Amongst military personnel, the success rates of this surgery can be as low as 20%, presenting a challenge in determining whether surgery is worthwhile. In this study, the data of 132 fasciotomies for CECS was analysed and using combinatorial feature selection methods, coupled with input from clinicians, identified a set of key clinical features contributing to the occupational outcomes of surgery. Features were utilised to develop a machine learning model for predicting return-to-work outcomes 12-months post-surgery. An AUC of 0.85 ± 0.08 was achieved using a linear-SVM, trained using 6 features (height, mean arterial pressure, pre-surgical score on the exercise-induced leg pain questionnaire, time from initial presentation to surgery, and whether a patient had received a prior surgery for CECS). To facilitate trust and transparency, interrogation strategies were used to identify reasons why certain patients were misclassified, using instance hardness measures. Model interrogation revealed that patient difficulty was associated with an overlap in the clinical characteristics of surgical outcomes, which was best handled by XGBoost and SVM-based models. The methodology was compiled into a machine learning framework, termed AITIA, which can be applied to other clinical problems. AITIA extends the typical machine learning pipeline, integrating the proposed interrogation strategy, allowing to user to reason and decide whether to trust the developed model based on the sensibility of its decision-making.