Utility of a novel point-of-care test in detecting coronary artery disease following negative nuclear testing: a case series.

Background: Coronary artery disease (CAD) often presents diagnostic challenges, especially in patients with atypical chest pain that persists despite negative non-invasive testing. For instance, nuclear stress testing, although widely used, may yield false-negative results, particularly in cases of balanced ischaemia. Accurate diagnostics are essential to avoid delays in diagnosis and treatment, particularly in the presence of negative nuclear testing. Case summary: We present five cases of intermediate- to high-risk patients with ongoing symptoms (chest pressure/discomfort/pain, nausea, shortness of breath, and exertional fatigue) and negative nuclear stress tests, who were ultimately diagnosed with significant CAD via invasive coronary angiography. In each case, CorVista CAD, a novel point-of-care test for CAD (POC-CAD) that analyses electrical and haemodynamic signals using machine learning, was contributory in raising the probability of disease, prompting invasive evaluation via coronary angiography and guiding appropriate management strategies. Discussion: This case series demonstrates the utility of POC-CAD as a non-invasive tool in the diagnostic pathway for patients with ongoing symptoms despite negative nuclear stress testing. The findings highlight limitations of nuclear stress testing, particularly its reduced sensitivity in multi-vessel disease. POC-CAD may help identify high-risk patients who would otherwise be missed, facilitating timely intervention. Integration of novel diagnostics such as POC-CAD into multimodal assessment strategies could improve accuracy and reduce diagnostic delays in CAD.