DOI: 10.1161/circ.148.suppl_1.15181 ISSN: 0009-7322

Abstract 15181: Sensitivity and Specificity of the Artificial Intelligence-Based 5-Lead 3D Vectorcardiography in Patients With Suspected or Confirmed Coronary Heart Disease

Caroline Schmidt-Lucke, Betty Lischke, Eugenia Weber, Daniel Günther, Anne Schomoeller, Henning Steen, André Schmidt-Lucke
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Introduction: Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI) offers additional information over 12-lead electrocardiography (ECG) identifying coronary vascular disease (CVD) in need for coronary intervention. Hypothesis: We tested the hypothesis of 5L3DVCG-AI being able to detect patients with mild to overt signs and / or history of CVD.

Methods: In this monocentric retrospective study, consecutive data of 331 patients with 5L3DVCG-AI were included. The Perfusion (P)-Factor for cardiac ischaemic pathologies, based on the P-Index including 731 parameters and in-house features calculated in time and frequency domains (e.g. beat moments), classified patients as high, medium or low risk for CVD . Diagnosis of CVD was based upon current guidelines by 2 independent blinded cardiologists and categorised as exclusion of CVD (control), mild signs or overt CVD. The P-Index was validated against clinical CVD. Cardiovascular risk factors (CVRF) were quantified with modified PROCAM score.

Results: Of 331 patients (m:w 60:40%, 50.0 ± 19.8 years) of mixed ethnicity and moderate CVRF (2.1 ± 1.2), 71% were controls, 20% had mild signs of CVD and 9% overt CVD. Follow-up period was 16.2 ± 7.5 months. CVRF was significantly higher in CVD compared to controls (2.7 ± 0.5 vs. 2.0 ± 1.2, p<0.05), and P-Factor correlated with number of CVRF (p<0.01), with significantly higher CVRF in higher P-factor (KW p<0.001). P-Factor indicated significantly more often higher risk in CVD compared to controls (0.78 ± 0.4 vs. 0.34 ± 0.48, p<0.01). P-Index from 5L3DVCG-AI at rest differentiated between CVD and controls (Chi2= 6.7, p=0.01). ROC curve showed correlation between P-Index and presence of CVD (R2=0.72, p<0.05). ECG at rest was not able to differentiate between CVD and controls.

Conclusions: These data extend the previous findings of 5L3DVCG-AI identifying CVD with cardiac ischaemia from those without to now differentiating healthy controls from CVD and those with higher risk for CVD. 5L3DVCG-AI may thus be a further scalable screening method to identify patients at risk for CVD in need for risk modification or further diagnostic procedures. Ongoing prospective large-scale clinical trials will have to confirm these data to verify diagnostic accuracy.

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