Evaluation of a Diagnostic Software for Coronary Artery Disease Using Retrospective CCTA Data (CODEX-1 Study)

Overview

The CODEX-1 study is a multicenter retrospective observational study designed to assess the diagnostic performance of a novel software application for coronary artery disease (CAD) evaluation. The application integrates automated stenosis detection, CT-derived fractional flow reserve (CT-FFR), and plaque quantification, all performed on-site. A total of 1,000 patients who previously underwent coronary computed tomography angiography (CCTA) and diagnostic invasive coronary angiography (ICA) and/or other non-invasive imaging will be included. The study compares the diagnostic outputs of the software to current clinical practice and expert adjudication, focusing on CAD-RADS categorization, prediction of the need for percutaneous coronary intervention (PCI), and reduction in unnecessary ICA procedures.

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Coronary computed tomography angiography (CCTA) has become a first-line diagnostic tool for patients with suspected CAD, and its utility can be further enhanced through the use of advanced software for automated assessment. The CODEX-1 study is a multicenter, retrospective, observational cohort study aimed at evaluating the diagnostic performance of a novel on-site software application integrating three key features: automated stenosis detection and CAD-RADS categorization, CT-derived fractional flow reserve (CT-FFR), and quantitative plaque analysis. The study will include 1,000 patients who underwent CCTA for CAD assessment between 2019 and 2024 at four European centers. All participants also have comparator diagnostic data available, such as invasive coronary angiography (ICA), stress MRI, or CCTA analyzed using alternative methods. The software's output will be compared against current clinical practice and expert consensus, with a focus on diagnostic accuracy, inter-reader variability, and the potential to reduce unnecessary ICA procedures. The study will not involve any patient intervention, and all data analyses will be performed offline using de-identified imaging datasets. The results are expected to provide evidence on the feasibility and accuracy of integrating multiple diagnostic tools into a single application, enabling faster and more consistent CAD diagnosis in clinical practice.