Cardiogoniometry for Detecting Coronary Artery Disease by CT Angiography

Cardiogoniometry is a technique to process and evaluate vectorcardiography from regular ECG acquisitions. Vectorcardiography has a long tradition in cardiology for providing comprehensive information on myocardial function and integrity. In recent years, computer assisted analysis has allowed automated interpretation of vectorcardiography with promising results in comparison to standard ECG for identifying patients with coronary heart disease. This study aims to investigate the utility of cardiogoniometry for noninvasively identifying patients who are at risk from coronary heart disease.

Cardiogoniometry is a technique to process and evaluate vectorcardiography from regular ECG acquisitions. Vectorcardiography has a long tradition in cardiology for providing comprehensive information on myocardial function and integrity. Compared to standard electrocardiography, vectorcardiography has shown to be more sensitive to detect structural and ischemic heart disease. Unfortunately, the interpretation of vectorcardiography is complex which has hindered its widespread application. In recent years, computer assisted analysis has allowed automated interpretation of vectorcardiography with promising results in comparison to standard ECG for identifying patients with ischemic heart disease. However, the underlying mechanisms and threshold of altered cardiac vectors in the presence of coronary artery disease are not well understood. This research aims at exploring the relationship of computer assisted analysis of vectorcardiography with the presence, extent, severity, and location of coronary artery disease in comparison to standard ECG evaluation. Furthermore, the investigators intent to follow up enrolled patients for the occurrence of adverse cardiovascular events for correlation with test findings. These data will provide comprehensive information on the diagnostic performance of noninvasive, inexpensive evaluation of cardiac vector loops for identifying patients at risk from coronary artery disease. Specifically, the study aims to: Compare the diagnostic accuracy of cardiogoniometry with standard ECG for detecting coronary artery disease as assessed by CT angiography Investigate the relationship between abnormal cardiogoniometry findings and the extent/severity/location of coronary artery disease by CT angiography Compare the intermediate term prognosis of patients according to cardiogoniometry, standard ECG, and CT findings

Accuracy of Identifying Patients With at Least One 50 Percent or Greater Coronary Artery Stenosis by CT Angiography

Area under curve (AUC) analysis is proposed to be used to determine the diagnostic accuracy of cardiogoniometry for detecting patients with coronary heart disease as defined by at least one 50% or greater stenosis on CT coronary angiography.

Area under the curve (AUC) analysis is proposed to be used to asses the diagnostic accuracy of CGM for identifying patients with any coronary atherosclerotic disease

Patient follow up data will be used to performance of CGM to identify patients who are at risk of suffering adverse cardiac events at follow up compared to coronary CT angiography using AUC analysis.

Inclusion Criteria: Patients age 18 or older who are referred for elective cardiac CT examination for evaluation of coronary artery disease Exclusion Criteria: hemodynamic instability history of anaphylactic contrast reaction inability of following breath hold instructions