CCTA, CACS and ECG Stress Testing in Patients With Suspected CAD: Precision Phenotyping and Financial Evaluation
CCTA, CACS and ECG Stress Testing in Patients With Suspected CAD: Precision Phenotyping and Financial Evaluation
The "DATASET-PRECISE", a 3-arm parallel randomized study, aims to provide new insights in
 risk stratification of patients with suspected CAD in the Greek population. The convergence
 of information derived from exercise ECG stress test, CACS, CCTA and metabolomic profiling in
 artificial intelligence algorithms describes in brief the main objective of this protocol.
 The design of the present proposal is based on current state-of-the-art literature,
 incorporating, however, additional innovative elements. It is about the first randomized
 study to be conducted in Greece, investigating the role of CCTA and CACS in CAD diagnosis and
 risk assessment. Moreover, the present protocol aims to integrate information on patients'
 metabolomic profiling. The process of the whole information by using artificial intelligence
 technology will lead to the development of new risk stratification algorithms, promoting
 further personalized diagnostic and therapeutic approach. Regarding Greece, this is the first
 prospectively enrolling medical database of this scale.
Symptom-based pre-test probability (PTP) scores that estimate the likelihood of obstructive
 CAD in stable chest pain have moderate accuracy. Appreciating and integrating the myriad risk
 predictors in an individual patient is a challenge for the clinician. To date, efforts to
 improve risk-stratification by using CCTA have largely relied upon luminal stenosis severity.
 The emphasis placed on this variable over others is in alignment with prior studies using
 invasive coronary angiography but ignores an array of other parameters important in the CAD
 pathogenic process, including coronary artery geometry, coronary calcium content, plaque
 composition, and plaque burden. As an increasing number of CCTA variables along with all
 clinical and metabolomic variables affecting risk need to be considered, the complexity of
 assessment increases, making it more difficult for a clinician to draw an overall conclusion
 regarding risk in an individual patient. Furthermore, the potential influence of unexpected
 interactions between several weaker predictors in an individual patient is often overlooked.
 In this study, we are seeking to develop an Artificial Intelligence (AI)-based model,
 utilizing clinical and metabolomic risk factors, serum biomarkers, CCTA imaging biomarkers,
 coronary artery calcium score and ECG stress testing variables, to predict the presence and
 the complexity of CAD. Moreover, we are trying to introduce an easy to use, cost-effective,
 clinical decision supporting tool. In clinical practice, the utilization of such an approach
 could improve risk stratification and help guide downstream personalized management. Briefly,
 the research objectives of the study are: 1. predict the risk of obstructive coronary artery
 disease, 2. quantify the burden and complexity of coronary atherosclerosis, 3. evaluate the
 prognostic risk in individual patients with suspected CAD, 4. provide more accurate diagnosis
 and risk stratification, 5. provide an easy to use, cost-effective clinical decision support
 tool, 6. improve decisions in low to intermediate risk patients regarding the need for
 further testing such as cardiac SPECT and invasive coronary angiography, as well as for the
 need for preventive therapies and finally, compare three diagnostic strategies in patients
 with suspected CAD in terms of efficacy and cost-effectiveness.

 The "DATASET-PRECISE" is a prospective, multi-center, open-label, 3-arm parallel randomized
 study. Following clinical consultation, participants will be approached and randomized 1:1:1
 to receive standard care plus ECG-stress testing or standard care plus ECG-stress testing and
 CACS or standard care plus ≥ 64-multidetector CCTA and CACS (Collaborating Organizations: 1st
 Cardiology Department of AUTH, 1st Cardiology Department of NKUA, Lefkos Stavros-The Athens
 Clinic & Affidea Kozani Cardiac Imaging Center). Randomization will be conducted using a
 web-based system to ensure allocation concealment. The trial will enroll consecutive patients
 with stable symptoms and suspected CAD admitted to study clinical sites over a period of 12
 months. Patients with a previous history of CAD and/or prior revascularization will be
 excluded. Subjects will undergo screening during the first day of examination, a 5ml blood
 sample will be collected one minute prior examination for metabolomic analysis (collaboration
 with the Lab. of Bioanalysis & Toxicology, School of Medicine, AUTH) and will be followed for
 18 months afterwards. The overall recruitment period is expected to last 12 months. The
 estimated total duration of the study from first patient screened to last patient last visit
 is 30 months.

 Based on previous studies for 80% power at a two-sided P value of 0.05, we will need to
 recruit about 250 patients per group to detect a relative reduction in the combined MACE rate
 (cardiac death, non-fatal myocardial infarction, revascularization or chest-pain
 rehospitalization) of 10% in the CCTA arm. A sample size of N = 900 patients is a pragmatic
 approach for such a first clinical study in the Greek population. Health service costs will
 be assigned to the type and intensity of resource use, measured by the number of diagnostic
 and therapeutic procedures or interventions, medications, hospital clinic attendances and
 hospitalization episodes from randomization to 18 months of follow-up. Costs will be
 attributed to the need for: 1. additional invasive or noninvasive imaging, 2. drug therapy,
 3. coronary revascularization and 4. hospitalization for chest pain.
Inclusion Criteria:

 1. Patients with stable symptoms and low to intermediate probability of coronary artery
 disease (CAD) referred for evaluation

 2. Patients without known history of CAD

 3. Patients older than 18 years

 4. Patients giving voluntary written consent to participate in the study

 5. Subject is willing to comply with study follow-up requirements

 Exclusion Criteria:

 1. Patients with a previous history of CAD

 2. Patients who refuse to give written consent for participation in the study

 3. In the investigator's opinion, subject will not be able to comply with the follow-up
 requirements

 4. Known pregnancy

 5. Subject has a known allergy to contrast agent that cannot be adequately pre-medicated

 6. Inability or unwilling to undergo computed tomography scanning, such as exceeding
 weight tolerance of scanner

 7. Severe renal failure (estimated Glomerular Filtration Rate-eGFR <30 mL/min)