Coronary Artery Disease
Conditions
Keywords
coronary angiography, ventricular ejection fraction, coronary artery disease
Brief summary
This study is primarily designed to collect resting phase signals from eligible subjects using the Phase Signal Recorder (PSR) prior to coronary angiography to machine learn and test an algorithm for detecting the presence of significant coronary artery disease in symptomatic adult patient.
Detailed description
This clinical protocol is primarily designed to collect resting phase signals from eligible subjects using the Phase Signal Recorder (PSR) prior to coronary angiography to machine learn and test an algorithm for detecting the presence of significant coronary artery disease in symptomatic adult patient. In addition to this primary objective, machine-learned algorithms will be developed and tested to report the left ventricular ejection fraction and to identify the location of significant coronary artery disease. The performance of the machine-learned algorithm will be evaluated using a comparative paired trial design. In this study, the physician will not be provided the results of analysis performed and, accordingly, the results of the analyses will not be used to guide treatment decisions for the patient.
Interventions
DEVICEPhase Signal Recorder
The cPSTA System is a medical device system that uses passive tomography to analyze a patient's phase space data to identify the presence of significant coronary artery disease. The cPSTA System consists of several components that work together to obtain, transmit, analyze the data, and display the results, including the Phase Signal Acquisition System (PSAQ System), which is the Phase Signal Recorder (PSR) and the Phase Signal Data Repository (PSDR); analytical software; and secure web portal. For this study only the PSAQ System is used by the clinical site for the purposes of acquiring and transmitting the signal.
Sponsors
Analytics For Life
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
21 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥21 years of age at the time of screening 2. Meets criteria for coronary angiography 3. Scheduled to undergo cardiac catheterization with coronary angiography 4. Ability to understand the requirements of the study and to provide written informed consent
Exclusion criteria
1. Prior documented myocardial infarction (MI) 2. Prior coronary artery bypass grafting (CABG) or previous coronary interventions (PCI) 3. Indication for invasive coronary angiography other than to assess for obstructive CAD (e.g., arrhythmia, cardiomyopathy, valvular abnormality) 4. Previous heart valve replacement 5. Previous sustained or paroxysmal atrial or ventricular arrhythmia 6. Infiltrative myocardial disease (Amyloid, Sarcoid, Right ventricular dysplasia) 7. Presence of cardiac implantable electronic device (CIED), including implantable cardioverter defibrillator (ICD), pacemaker (PM), implantable loop recorders and other monitors 8. Implantable Neuro-stimulators 9. Congenital Heart Disease 10. Pregnancy (breast feeding) 11. Currently taking any Type IA, IC or III antiarrhythmic 12. Any history of Amiodarone therapy 13. Clinically significant chest deformity (e.g., pectus excavatum or pectus carnitatum) 14. Breast implants 15. Neuromuscular Disease if the condition results in tremor or muscle fasciculations
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Machine-learned algorithm | 7 day | Develop and test a machine-learned algorithm which maximizes the area under the receiver-operating characteristic (AUC-ROC) curve to 0.70 (expected) and clinically significant sensitivity and specificity to detect CAD when compared to clinical diagnosis of significant coronary artery disease (i.e., presence of ≥70% stenosis by angiography or reduced fraction flow of \<=0.80) in at least one of the major coronary arteries (LMA, LAD, RCA, or LCX) or their distributions. |
Outcome results
None listed