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

TL;DR

A new point-of-care test helps detect coronary artery disease in patients with negative nuclear stress tests and ongoing symptoms.

Contribution

A novel point-of-care test (POC-CAD) is shown to improve CAD detection when nuclear stress tests are negative.

Findings

Five patients with negative nuclear stress tests were found to have significant CAD via invasive angiography.

POC-CAD identified high-risk patients who would have been missed by nuclear stress testing alone.

POC-CAD may improve diagnostic accuracy and reduce delays in CAD diagnosis.

Abstract

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.

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.

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.

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