UWA chair of Cardiology, Fiona Stanley Hospital-based Perkins researcher and scientific officer of medtech company Artrya Professor Girish Dwivedi has been exploring the potential of AI in a busy metropolitan emergency department.
The project aims to develop an AI-driven system that will give a personalised rating of the appropriateness of various advanced cardiovascular investigations for patients who have presented to the ED with chest pain.
“Investigations are an essential component of patient care and many patients who present to the ED with chest pain will undergo advanced cardiovascular imaging. There are multiple investigation modalities available, each with their unique strengths and weaknesses. The decision on which investigation is most appropriate for the patient has become increasingly complex,” Prof Dwivedi said.
“There is growing recognition that there is significant variation in investigation practices, with associated patient harm, healthcare waste and economic cost.”
Cardiac exercise stress testing offers a non-invasive approach for those with suspected coronary artery disease. However, up to one-third of exercise stress test results are either inconclusive or non-diagnostic, resulting in significant resource wastage.
The AI system, developed by the multidisciplinary research team using demographic and pre-test clinical information, can accurately predict exercise stress test results and could be used to identify patients who would have inconclusive or non-diagnostic results.
“The proposed system could be used as a personalised decision-support tool by clinicians for optimising the diagnostic test selection for heart disease patients, which would reduce health care expenditure by reducing non-diagnostic or inconclusive tests,” he said.
Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of stroke and bleeding risks.
As part of the project, the investigators developed AI models that outperformed the existing clinical risk stratification scores for predicting the risk of major bleeding and death in AF patients.
“Once validated in other studies, this has the potential to assess the utility as well as the risk with blood thinners in patients with AF, one of the most common arrhythmias seen in clinical practice,” he said.
Recently, Prof Dwivedi’s team at UWA received $896,606 through a Medical Research Future Fund Frontiers in Health grant to develop a tool to better predict the risk of coronary heart disease from heart CT scans.
“Our AI-based risk prediction system will identify patients at risk of heart attack and also those who would most benefit from treatment,” he said.