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Petch J, Tabja Bortesi JP, Sheth T, Natarajan M, Pinilla-Echeverri N, Di S, Bangdiwala SI, Mosleh K, Ibrahim O, Bainey KR, Dobranowski J, Becerra MP, Sonier K, Schwalm JD
Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial
Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-risk Patients Screened with Artificial Intelligence (CarDIA-AI): Protocol for a Randomized Controlled Trial
Jeremy Petch;
Juan Pablo Tabja Bortesi;
Tej Sheth;
Madhu Natarajan;
Natalia Pinilla-Echeverri;
Shuang Di;
Shrikant I. Bangdiwala;
Karen Mosleh;
Omar Ibrahim;
Kevin R. Bainey;
Julian Dobranowski;
Maria P. Becerra;
Katie Sonier;
Jon-David Schwalm
ABSTRACT
Background:
Invasive coronary angiography (ICA) is the gold standard in the diagnosis of coronary artery disease (CAD). Being invasive, it carries rare but serious risks including myocardial infarction, stroke, major bleeding and death. A large proportion of elective outpatients undergoing ICA have non-obstructive CAD, highlighting suboptimal use of this test. Coronary computed tomographic angiography (CCTA) is a non-invasive option that provides similar information with less risk, and is recommended as a first-line test for patients with low-to-intermediate risk of CAD. Leveraging artificial intelligence (AI) to appropriately direct patients to ICA or CCTA based on predicted probability of disease may improve efficiency and safety of diagnostic pathways.
Objective:
The CarDIA-AI study aims to evaluate whether AI-based risk assessment for obstructive CAD implemented within a centralized triage process can optimize the use of ICA in outpatients referred for non-urgent ICA.
Methods:
CarDIA-AI is a pragmatic, open-label, superiority randomized controlled trial involving two Canadian cardiac centres. A total of 252 adults referred for elective outpatient ICA will be randomized 1:1 to usual care (directly proceeding to ICA) or to triage using an AI-based CAD screening intervention. Participants in the intervention arm will have their ICA referral forms and medical charts reviewed and select details entered into a decision support tool that uses a LightGBM model to predict the probability of obstructive CAD and recommends CCTA or ICA accordingly. The primary outcome is the proportion of normal or non-obstructive CAD diagnosed via ICA. Secondary outcomes include the number of angiograms avoided and the diagnostic yield of ICA.
Results:
Recruitment began on January 9, 2025 and is expected to conclude in mid-2025. We expect to submit the results for publication in early 2026.
Conclusions:
CarDIA-AI will be the first randomized controlled trial employing AI to optimize patient selection for CCTA versus ICA, potentially improving diagnostic efficiency, avoiding unnecessary complications of ICA, and improving healthcare resource utilization. Clinical Trial: ClinicalTrials.gov NCT06648239; https://clinicaltrials.gov/study/NCT06648239/
Citation
Please cite as:
Petch J, Tabja Bortesi JP, Sheth T, Natarajan M, Pinilla-Echeverri N, Di S, Bangdiwala SI, Mosleh K, Ibrahim O, Bainey KR, Dobranowski J, Becerra MP, Sonier K, Schwalm JD
Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial