Abstract
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.
Highlights
Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms
Whilst fasciotomies have proven successful amongst civilian populations, enabling more than 75% of athletes to return to sport[10,11,12,13,14,15], the same cannot be said for military personnel who have been described as having less reliable o utcomes[16]
This paper presents the development of a machine learning model to predict the return-to-work outcomes of military personnel following a corrective fasciotomy for CECS, using routinely collected pre-surgical patient data from a military rehabilitation facility
Summary
Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms. Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs[1,2,3,4] and is prevalent in individuals who partake in activities such as walking, running and marching whilst carrying load[5]. Waterman et al.[19] applied binary logistic regression analysis to a large dataset of active military personnel, to identify variables that are associated with surgical failure. Factors that are associated with surgical outcomes were identified, the predictive value of these factors was not evaluated Whilst methods such as logistic regression are suitable for identifying relationships and performing basic predictions, through the application of machine learning, generalised linear models can be o utperformed[20]
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