AIMS
This project will first create a large database of synthetic ACS data, by using an initial pool of patient data. Synthetic data is artificially generated data, made by a computer, which is used to replicate the original components of real data. It is not actual patient data, but it reflects the patient population. Synthetic data and then patient data will be used to build diagnostic and predictive models which can assess whether a person is likely to have ACS and how severe the ACS is likely to be, to help with clinical decision making.
BACKGROUND
Acute Compartment Syndrome (ACS) is a rare condition developed within a bundle of muscles after a sudden, severe injury. The muscles become damaged and swell, increasing pressure in the muscle bundle, reducing the blood supply to the area. Patients with ACS experience sudden pain in the affected injury area and require immediate emergency surgery (known as a fasciotomy) to release the pressure in the muscles. Late diagnosis increases the risk of losing a limb, permanent disability, or loss of life. Misdiagnosis can also be costly: performing potentially unnecessary surgery can reduce the quality of life of a patient, as skin grafts are needed, and permanent nerve and muscle damage can occur.
Diagnosing ACS remains very difficult and better tools are needed to identify the condition and assess its severity.
RESEARCH
The overall aim of the project is to improve the diagnosis of ACS and reduce the number of unnecessary emergency surgeries. The researchers aim to use PIONEER’s data to build a predictive tool which surgeons can use to assess the likelihood of ACS following an injury.
To use the tool, the surgeon would input information about the patient, such as the age and health of the patient and the nature and size of the injury. The tool would also include symptoms such as pain levels, and specific ACS tests, including blood test results, the amount of pressure inside the limb and the amount of oxygen in the affected muscles. The tool would then predict how likely ACS was and whether surgery was needed.
PATIENT INVOLVEMENT
The researchers have sought the perspectives of patients on this critical issue, and have plans in place to conduct a number of public engagement activities to increase the awareness of the problems of ACS diagnosis. Feedback from these events will be used to influence the project’s future direction.
APPROVAL
This project was supported unanimously by the PIONEER Data Trust Committee.
This work is led by Dr Pranav Vasanthi, EPSRC Research Fellow at the University of Birmingham.
