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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.
Full description
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.
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900 participants in 3 patient groups
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Georgios Rampidis, MD, MSc
Data sourced from clinicaltrials.gov
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