Investigation of Acute Compartment Syndrome
Early diagnosis of circulatory disorders would provide the practitioner/diagnostician/surgeon with several patient-specific treatment options which would significantly reduce the mortality rates. This project would focus on acute compartment syndrome (ACS) which is an emergency orthopaedic condition. A traumatic injury of the limbs causes an increased intercompartmental pressure (ICP) which leads to decreased blood perfusion and eventually necrosis (cell death) of the surrounding muscle tissues due to lack of oxygen and nutrients. ACS costs the NHS is almost 40M annually from shin fractures alone. The current treatment method involves a highly invasive surgical procedure called fasciotomy which must be performed within eight hours of the accident to avoid permanent muscle and nerve damage. Further, delays in treatment can cause severe kidney damage and death. Hence accurate diagnosis of ACS is critical. The current ‘gold standard’ of ACS diagnosis is based on clinical investigation of the patient such as pain, obvious swelling, loss of sensory perceptions etc. There is a lack of quantitative methods to objectively diagnose ACS using anatomical and physiological markers. The overall goal of the project is to develop a statistical uncertainty-based model using currently available clinical data to provide the clinicians with a powerful tool that predicts the error/likelihood of ACS development. This would enable the clinicians to implement data-driven decisions and ultimately produce patient-specific therapies.
A two-pronged in-vitro modelling approach will be used to develop the uncertainty based statistical model for diagnosing ACS. In the first step, a non-cellular in-vitro model of ACS will be developed. This would involve fabricating a microfluidic model of the ‘artery-capillary-vein’ microvasculature system using multi-layer soft-lithography. In the second step, human umbilical vein endothelial cells (HUVECs) will be cultured inside the microfluidic device and the influence of different flow patterns and pressure regimes will be analysed In addition to this, Computational fluid dynamics (CFD) models will be used to augment the in-vitro data which would then be correlated with the clinical data. It is envisaged that upon reaching the ‘proof-of-concept’ stage, the uncertainty-based statistical model will accurately diagnose ACS which will be assessed via a clinical trial to translate the technology to the clinic. Further, it is expected that PREMIERE would engage with clinicians for feedback (for eventual technological refinement to keep the technology clinically useful. Further, the technology to be developed from this project can also be applied to other circulatory conditions that have a huge impact on the NHS (coronary heart disease/atherosclerosis which results in 73,000 deaths annually).
