5-lead 3D-vectorcardiography (5L3DVCG-AI), including a dorsal electrode with ensemble of artificial neural networks (ANN) offers additional information over standard 12-lead electrocardiography (ECG) in the detection of cardiac ischaemia. Artificial Intelligence (AI) and access to a massive global clinical data repository have the potential to boost the performance of VCG beyond conventional techniques. VCG offers ease-of-use at rest and is therefore a promising candidate for becoming a new screening tool for coronary artery diseases (CAD). We, thus, tested the hypothesis of 3D-VCG being able to differentiate between patients with lower and high risk of cardiovascular disease (CVD) and identify those requiring coronary interventions.

In this exploratory study, we analysed patients in a national multicentre trial. Patients were recruited from general cardiologists’ praxis and radiologist centre with patients referred for further diagnosis of suspected or confirmed CVD. Based on the Perfusion (P)-Factor of the 5-lead 3D- vectorcardiography, including 731 parameters, e.g., QRS-T angle and in-house features calculated in time and frequency domains, such as beat moments a P-Index is calculated. Based on the P-Index, patients were either classified as high, medium, or low risk for CVD (medium + high defined as high CVD-risk). Confirmation of CAD was performed according to the practitioners’ discretion blinded to the P-Index. Number of risk factors (mod. PROCAM score) were compared between the high- and low-risk group using an independent t-test.

The 468 patients in this sample (m:w 61:39, age: 66 [40-87] years) showed 3.6 [0 - 7] CVRF, 16% had arrhythmias or conduction disturbances (AF, PM, BBB), 24% pts. had consecutive PCI or CABG. 62% were classified as low while 38% were classified as high risk for CVD by the P-Index. Number of cardiovascular risk factors was significantly higher in the high-risk CVD-group as defined by P-Index compared to low CVD-risk (4.0 [3.0 – 5.0] vs. 3.5 [2.0 – 4.0], p < 0.05). P-Index differentiated between suspected CVD with or without consequent PCI or CABG (Chi2 = 4.02, p<0.05).

The 5L3DVSG-AI is an innovative diagnostic tool that can help determine a patient's cardiovascular risk profile in resting condition for clinical and research purposes. Furthermore, the P-Index identified those with significant stenoses in need for coronary intervention and may thus be a further scalable screening method to differentiate patients with consequent interventional approach or need for risk modification. These preliminary data will have to be confirmed in a prospective, large-scale performance study to verify the diagnostic accuracy.