Coronary Artery Disease Learning and Algorithm Development (CADLAD) Clinical Study
The cardiac Phase Space Tomography Analysis (cPSTA) System is a medical device being developed to collect resting phase signals from symptomatic adult patients to machine learn and test an algorithm for detecting the presence of significant coronary artery disease. The cPSTA System will provide information to the physician for determining presence and significance of coronary artery disease and consists of several components:
- The Phase Signal Acquisition System (PSAQ) consists of the Phase Signal Recorder (PSR), a hand-held instrument that obtains and transmits phase signal data, and the Phase Signal Data Repository (PSDR), a cloud-based data repository.
- An analytical engine is software that analyzes the acquired data.
- A secure web portal for accessing reports displays results.
In the CADLAD study, data will be acquired in up to 2,500 subjects prior to coronary angiography. Their catheterization results for a clinical diagnosis of significant coronary artery disease (i.e., either a ≥ 70% stenosis or a reduced fractional flow rate of <0.80) in the major coronary arteries and their distributions, including the left main artery (LMA), left anterior descending artery (LAD), circumflex artery (LCX), and the right coronary artery (RCA) will be used to develop and compare the performance of the machine learned algorithm.
The study involves two stages:
- Stage I is a prospective, non-randomized trial to develop a machine learned algorithm to detect and assess significant coronary artery disease (CAD) using paired phase signals with clinical outcomes data.
- Stage II is a prospective, blinded, non-randomized, paired comparison trial to test the machine-learned algorithm from Stage I to detect significant CAD with a clinical diagnosis of significant CAD as assessed during a catheterization procedure (i.e., either a ≥ 70% stenosis or a reduced fractional flow rate of <0.80).