New data from the ongoing CADLAD (Coronary artery disease learning and algorithm development) study will be presented at the 2017 Transcatheter Cardiovascular Therapeutics (TCT) meeting (29 October – 2 November, Denver, USA) this week. The study is evaluating the diagnostic performance of the cardiac phase space tomography analysis (cPSTA) system (Analytics4Life) in assessing cardiac health related to the presence of coronary artery disease.
The CADLAD study is a two-stage clinical trial at 13 sites in the USA. The cPSTA system, or CorVista, is a non-invasive, physician-directed diagnostic test that uses a hand-held device to scan intrinsic signals from the body without radiation, contrast agents, or cardiac stress. The signal data is then transmitted to a secure, cloud-based repository for analysis, and a report is generated to help physicians assess the presence of coronary artery disease.
The ongoing study is designed to test the utility of PSTA in a large population and other important subgroups. Enrolment into the study’s first stage, focused on product development, is complete with enrolment in the study’s second stage finishing before the end of this year. Results will be available early 2018 and will support the company’s US FDA regulatory application.
Don Crawford, CEO, Analytics 4 Life, comments: “We are encouraged by these initial results which suggest that our cardiac imaging technology has clinically significant utility in assessing coronary artery disease, without the need for radiation, exercise or pharmacologic stress. Conventional coronary artery disease detection pathways may be less accurate in specific populations, such as obese, elderly and female patients, but these early results show a promising potential for alternative technology.”
Details of the data presentations at the TCT 2017 scientific symposium are as follows:
TCT 154: Gender Based Assessment of Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms
30 October, 10.42am; MDT
TCT 177: Assessing Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms in Obese and Elderly Subjects
30 October, 11.18am; MDT
Coronary angiography results were paired with cPSTA data from 512 patients to generate a machine-learned algorithm to assess for significant coronary artery disease.
- A separate verification cohort of 94 patients was used to prospectively test the accuracy
- Analyses focused on total verification population and on CADLAD by gender (male vs. female), age (> 65 vs. < 65 years of age), and obesity (BMI of > 30 vs. < 30)
- Results suggest that the cPSTA System performs well overall across the verification population and respective subpopulations
