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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Enrolled Subjects (PSR) | Other | Patients who meet the study's inclusion and exclusion criteria, including signing the informed consent form, subjects will undergo signal acquisition prior to their scheduled cardiac catheterization on the day of the procedure. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Phase Signal Recorder | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Machine-learned algorithm | 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. | 7 day |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| William E Sanders, Jr., MD MBA FHRS | Analytics For Life | Study Director |
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| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |