<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04424121</url>
  </required_header>
  <id_info>
    <org_study_id>DATASET PRECISE_01062020_03448</org_study_id>
    <nct_id>NCT04424121</nct_id>
  </id_info>
  <brief_title>CCTA, CACS and ECG Stress Testing in Patients With Suspected CAD: Precision Phenotyping and Financial Evaluation</brief_title>
  <acronym>DATASET</acronym>
  <official_title>Cardiac CT, Calcium Scoring and ECG Stress Testing in Patients With Suspected Coronary Artery Disease: Precision Phenotyping and Financial Evaluation (The DATASET-PRECISE Randomized Trial)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Aristotle University Of Thessaloniki</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Lefkos Stavros The Athens Clinic</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>National and Kapodistrian University of Athens</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Aristotle University Of Thessaloniki</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The &quot;DATASET-PRECISE&quot;, a 3-arm parallel randomized study, aims to provide new insights in&#xD;
      risk stratification of patients with suspected CAD in the Greek population. The convergence&#xD;
      of information derived from exercise ECG stress test, CACS, CCTA and metabolomic profiling in&#xD;
      artificial intelligence algorithms describes in brief the main objective of this protocol.&#xD;
      The design of the present proposal is based on current state-of-the-art literature,&#xD;
      incorporating, however, additional innovative elements. It is about the first randomized&#xD;
      study to be conducted in Greece, investigating the role of CCTA and CACS in CAD diagnosis and&#xD;
      risk assessment. Moreover, the present protocol aims to integrate information on patients'&#xD;
      metabolomic profiling. The process of the whole information by using artificial intelligence&#xD;
      technology will lead to the development of new risk stratification algorithms, promoting&#xD;
      further personalized diagnostic and therapeutic approach. Regarding Greece, this is the first&#xD;
      prospectively enrolling medical database of this scale.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Symptom-based pre-test probability (PTP) scores that estimate the likelihood of obstructive&#xD;
      CAD in stable chest pain have moderate accuracy. Appreciating and integrating the myriad risk&#xD;
      predictors in an individual patient is a challenge for the clinician. To date, efforts to&#xD;
      improve risk-stratification by using CCTA have largely relied upon luminal stenosis severity.&#xD;
      The emphasis placed on this variable over others is in alignment with prior studies using&#xD;
      invasive coronary angiography but ignores an array of other parameters important in the CAD&#xD;
      pathogenic process, including coronary artery geometry, coronary calcium content, plaque&#xD;
      composition, and plaque burden. As an increasing number of CCTA variables along with all&#xD;
      clinical and metabolomic variables affecting risk need to be considered, the complexity of&#xD;
      assessment increases, making it more difficult for a clinician to draw an overall conclusion&#xD;
      regarding risk in an individual patient. Furthermore, the potential influence of unexpected&#xD;
      interactions between several weaker predictors in an individual patient is often overlooked.&#xD;
      In this study, we are seeking to develop an Artificial Intelligence (AI)-based model,&#xD;
      utilizing clinical and metabolomic risk factors, serum biomarkers, CCTA imaging biomarkers,&#xD;
      coronary artery calcium score and ECG stress testing variables, to predict the presence and&#xD;
      the complexity of CAD. Moreover, we are trying to introduce an easy to use, cost-effective,&#xD;
      clinical decision supporting tool. In clinical practice, the utilization of such an approach&#xD;
      could improve risk stratification and help guide downstream personalized management. Briefly,&#xD;
      the research objectives of the study are: 1. predict the risk of obstructive coronary artery&#xD;
      disease, 2. quantify the burden and complexity of coronary atherosclerosis, 3. evaluate the&#xD;
      prognostic risk in individual patients with suspected CAD, 4. provide more accurate diagnosis&#xD;
      and risk stratification, 5. provide an easy to use, cost-effective clinical decision support&#xD;
      tool, 6. improve decisions in low to intermediate risk patients regarding the need for&#xD;
      further testing such as cardiac SPECT and invasive coronary angiography, as well as for the&#xD;
      need for preventive therapies and finally, compare three diagnostic strategies in patients&#xD;
      with suspected CAD in terms of efficacy and cost-effectiveness.&#xD;
&#xD;
      The &quot;DATASET-PRECISE&quot; is a prospective, multi-center, open-label, 3-arm parallel randomized&#xD;
      study. Following clinical consultation, participants will be approached and randomized 1:1:1&#xD;
      to receive standard care plus ECG-stress testing or standard care plus ECG-stress testing and&#xD;
      CACS or standard care plus ≥ 64-multidetector CCTA and CACS (Collaborating Organizations: 1st&#xD;
      Cardiology Department of AUTH, 1st Cardiology Department of NKUA, Lefkos Stavros-The Athens&#xD;
      Clinic &amp; Affidea Kozani Cardiac Imaging Center). Randomization will be conducted using a&#xD;
      web-based system to ensure allocation concealment. The trial will enroll consecutive patients&#xD;
      with stable symptoms and suspected CAD admitted to study clinical sites over a period of 12&#xD;
      months. Patients with a previous history of CAD and/or prior revascularization will be&#xD;
      excluded. Subjects will undergo screening during the first day of examination, a 5ml blood&#xD;
      sample will be collected one minute prior examination for metabolomic analysis (collaboration&#xD;
      with the Lab. of Bioanalysis &amp; Toxicology, School of Medicine, AUTH) and will be followed for&#xD;
      18 months afterwards. The overall recruitment period is expected to last 12 months. The&#xD;
      estimated total duration of the study from first patient screened to last patient last visit&#xD;
      is 30 months.&#xD;
&#xD;
      Based on previous studies for 80% power at a two-sided P value of 0.05, we will need to&#xD;
      recruit about 250 patients per group to detect a relative reduction in the combined MACE rate&#xD;
      (cardiac death, non-fatal myocardial infarction, revascularization or chest-pain&#xD;
      rehospitalization) of 10% in the CCTA arm. A sample size of N = 900 patients is a pragmatic&#xD;
      approach for such a first clinical study in the Greek population. Health service costs will&#xD;
      be assigned to the type and intensity of resource use, measured by the number of diagnostic&#xD;
      and therapeutic procedures or interventions, medications, hospital clinic attendances and&#xD;
      hospitalization episodes from randomization to 18 months of follow-up. Costs will be&#xD;
      attributed to the need for: 1. additional invasive or noninvasive imaging, 2. drug therapy,&#xD;
      3. coronary revascularization and 4. hospitalization for chest pain.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Not yet recruiting</overall_status>
  <start_date type="Anticipated">January 1, 2021</start_date>
  <completion_date type="Anticipated">December 31, 2023</completion_date>
  <primary_completion_date type="Anticipated">June 30, 2023</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <intervention_model_description>Open-label, 3-arm parallel randomized study</intervention_model_description>
    <primary_purpose>Diagnostic</primary_purpose>
    <masking>None (Open Label)</masking>
    <masking_description>Open-label, 3-arm parallel randomized study</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Major Adverse Cardiac Events (MACE)</measure>
    <time_frame>18 months</time_frame>
    <description>Defined as cardiac death, non-fatal myocardial infarction or revascularization with percutaneous coronary intervention or coronary artery bypass graft surgery. Revascularization procedures within 6 weeks after the index CCTA will be excluded because they may be triggered by the CCTA findings per se</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Chest-pain rehospitalization</measure>
    <time_frame>18 months</time_frame>
    <description>Frequency (%) of chest-pain rehospitalization</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Frequency of angina [Seattle Angina Questionnaire (SAQ)]</measure>
    <time_frame>6 months</time_frame>
    <description>The SAQ quantifies patients' physical limitations caused by angina, the frequency of and recent changes in their symptoms, their satisfaction with treatment, and the degree to which they perceive their disease to affect their quality of life. Scores range from 0 to 100, where higher scores indicate better function (less physical limitation, less angina and better quality of life)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Frequency of angina [Seattle Angina Questionnaire (SAQ)]</measure>
    <time_frame>12 months</time_frame>
    <description>The SAQ quantifies patients' physical limitations caused by angina, the frequency of and recent changes in their symptoms, their satisfaction with treatment, and the degree to which they perceive their disease to affect their quality of life. Scores range from 0 to 100, where higher scores indicate better function (less physical limitation, less angina and better quality of life)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Frequency of angina [Seattle Angina Questionnaire (SAQ)]</measure>
    <time_frame>18 months</time_frame>
    <description>The SAQ quantifies patients' physical limitations caused by angina, the frequency of and recent changes in their symptoms, their satisfaction with treatment, and the degree to which they perceive their disease to affect their quality of life. Scores range from 0 to 100, where higher scores indicate better function (less physical limitation, less angina and better quality of life)</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Generic health status [Medical Outcomes Study 12-Item Short Form (SF-12)]</measure>
    <time_frame>12 months</time_frame>
    <description>The Medical Outcomes Study 12-Item Short Form (SF-12) is a general health questionnaire and is computed using the scores of 12 questions ranging from 0 to 100, where 0 indicates the lowest level of health and 100 indicates the highest level of health</description>
  </secondary_outcome>
  <number_of_arms>3</number_of_arms>
  <enrollment type="Anticipated">900</enrollment>
  <condition>Stable Angina</condition>
  <condition>Coronary Artery Disease</condition>
  <condition>Atherosclerosis</condition>
  <condition>Coronary Artery Calcification</condition>
  <arm_group>
    <arm_group_label>Standard of care plus ECG Stress Testing</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Participants will be approached and randomized to receive standard care plus ECG-stress testing</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Standard of care plus ECG Stress Testing and CACS</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Participants will be approached and randomized to receive standard care plus ECG-stress testing and coronary artery calcium scoring</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Standard of care plus CCTA</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Participants will be approached and randomized to receive standard care plus ≥ 64 multidetector coronary computed tomography angiography</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>CCTA</intervention_name>
    <description>Coronary Computed Tomography Angiography</description>
    <arm_group_label>Standard of care plus CCTA</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Patients with stable symptoms and low to intermediate probability of coronary artery&#xD;
             disease (CAD) referred for evaluation&#xD;
&#xD;
          2. Patients without known history of CAD&#xD;
&#xD;
          3. Patients older than 18 years&#xD;
&#xD;
          4. Patients giving voluntary written consent to participate in the study&#xD;
&#xD;
          5. Subject is willing to comply with study follow-up requirements&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Patients with a previous history of CAD&#xD;
&#xD;
          2. Patients who refuse to give written consent for participation in the study&#xD;
&#xD;
          3. In the investigator's opinion, subject will not be able to comply with the follow-up&#xD;
             requirements&#xD;
&#xD;
          4. Known pregnancy&#xD;
&#xD;
          5. Subject has a known allergy to contrast agent that cannot be adequately pre-medicated&#xD;
&#xD;
          6. Inability or unwilling to undergo computed tomography scanning, such as exceeding&#xD;
             weight tolerance of scanner&#xD;
&#xD;
          7. Severe renal failure (estimated Glomerular Filtration Rate-eGFR &lt;30 mL/min)&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Haralambos Karvounis, Prof. in Cardiology</last_name>
    <role>Study Chair</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Georgios Giannakoulas, Prof. in Cardiology</last_name>
    <role>Study Director</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Periklis Kounatiadis, MD, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Panagiotis Bamidis, Prof. in Bioinformatics</last_name>
    <role>Principal Investigator</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Georgios Rampidis, MD, MSc</last_name>
    <role>Principal Investigator</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Olga Deda, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_official>
    <last_name>Antonios Billis, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Aristotle University Of Thessaloniki, School of Medicine</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Georgios Rampidis, MD, MSc</last_name>
    <phone>2310994830</phone>
    <phone_ext>+30</phone_ext>
    <email>grampidi@auth.gr</email>
  </overall_contact>
  <location>
    <facility>
      <name>Lefkos Stavros The Athens Clinic</name>
      <address>
        <city>Athens</city>
        <country>Greece</country>
      </address>
    </facility>
    <contact>
      <last_name>Georgios Benetos, MD, PhD</last_name>
    </contact>
  </location>
  <location>
    <facility>
      <name>National and Kapodistrian University of Athens, School of Medicine</name>
      <address>
        <city>Athens</city>
        <country>Greece</country>
      </address>
    </facility>
    <contact>
      <last_name>Georgios Benetos, MD, PhD</last_name>
    </contact>
    <investigator>
      <last_name>Konstantina Aggeli, Prof. in Cardiology</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Vasiliki Katsi, MD, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Georgios Benetos, MD, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location>
    <facility>
      <name>Aristotle University of Thessaloniki, School of Medicine</name>
      <address>
        <city>Thessaloníki</city>
        <country>Greece</country>
      </address>
    </facility>
    <contact>
      <last_name>Georgios Rampidis, MD, MSc</last_name>
    </contact>
    <investigator>
      <last_name>Haralambos Karvounis, MD, PhD</last_name>
      <role>Principal Investigator</role>
    </investigator>
    <investigator>
      <last_name>Georgios Giannakoulas, MD, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Periklis Kounatiadis, MD, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Georgios Rampidis, MD, MSc</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Panagiotis Bamidis, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Olga Deda, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Antonios Billis, PhD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
    <investigator>
      <last_name>Konstantinos Michailidis, MD</last_name>
      <role>Sub-Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>Greece</country>
  </location_countries>
  <reference>
    <citation>Benz DC, Benetos G, Rampidis G, von Felten E, Bakula A, Sustar A, Kudura K, Messerli M, Fuchs TA, Gebhard C, Pazhenkottil AP, Kaufmann PA, Buechel RR. Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr. 2020 Sep-Oct;14(5):444-451. doi: 10.1016/j.jcct.2020.01.002. Epub 2020 Jan 13.</citation>
    <PMID>31974008</PMID>
  </reference>
  <reference>
    <citation>Benetos G, Buechel RR, Goncalves M, Benz DC, von Felten E, Rampidis GP, Clerc OF, Messerli M, Giannopoulos AA, Gebhard C, Fuchs TA, Pazhenkottil AP, Kaufmann PA, Grani C. Coronary artery volume index: a novel CCTA-derived predictor for cardiovascular events. Int J Cardiovasc Imaging. 2020 Apr;36(4):713-722. doi: 10.1007/s10554-019-01750-2. Epub 2020 Jan 1.</citation>
    <PMID>31894527</PMID>
  </reference>
  <reference>
    <citation>Rampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini Score calculation. Atherosclerosis. 2019 Aug;287:181-183. doi: 10.1016/j.atherosclerosis.2019.05.012. Epub 2019 May 10. No abstract available.</citation>
    <PMID>31104809</PMID>
  </reference>
  <reference>
    <citation>Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T, Agewall S, Dickstein K, Edvardsen T, Escaned J, Gersh BJ, Svitil P, Gilard M, Hasdai D, Hatala R, Mahfoud F, Masip J, Muneretto C, Valgimigli M, Achenbach S, Bax JJ; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020 Jan 14;41(3):407-477. doi: 10.1093/eurheartj/ehz425. No abstract available. Erratum In: Eur Heart J. 2020 Nov 21;41(44):4242.</citation>
    <PMID>31504439</PMID>
  </reference>
  <reference>
    <citation>Budoff MJ, Mayrhofer T, Ferencik M, Bittner D, Lee KL, Lu MT, Coles A, Jang J, Krishnam M, Douglas PS, Hoffmann U; PROMISE Investigators. Prognostic Value of Coronary Artery Calcium in the PROMISE Study (Prospective Multicenter Imaging Study for Evaluation of Chest Pain). Circulation. 2017 Nov 21;136(21):1993-2005. doi: 10.1161/CIRCULATIONAHA.117.030578. Epub 2017 Aug 28.</citation>
    <PMID>28847895</PMID>
  </reference>
  <reference>
    <citation>SCOT-HEART Investigators; Newby DE, Adamson PD, Berry C, Boon NA, Dweck MR, Flather M, Forbes J, Hunter A, Lewis S, MacLean S, Mills NL, Norrie J, Roditi G, Shah ASV, Timmis AD, van Beek EJR, Williams MC. Coronary CT Angiography and 5-Year Risk of Myocardial Infarction. N Engl J Med. 2018 Sep 6;379(10):924-933. doi: 10.1056/NEJMoa1805971. Epub 2018 Aug 25.</citation>
    <PMID>30145934</PMID>
  </reference>
  <reference>
    <citation>Hilvo M, Meikle PJ, Pedersen ER, Tell GS, Dhar I, Brenner H, Schottker B, Laaperi M, Kauhanen D, Koistinen KM, Jylha A, Huynh K, Mellett NA, Tonkin AM, Sullivan DR, Simes J, Nestel P, Koenig W, Rothenbacher D, Nygard O, Laaksonen R. Development and validation of a ceramide- and phospholipid-based cardiovascular risk estimation score for coronary artery disease patients. Eur Heart J. 2020 Jan 14;41(3):371-380. doi: 10.1093/eurheartj/ehz387.</citation>
    <PMID>31209498</PMID>
  </reference>
  <reference>
    <citation>von Felten E, Messerli M, Giannopoulos AA, Benz DC, Schwyzer M, Benetos G, Rampidis G, Patriki D, Kamani CH, Grani C, Fuchs TA, Pazhenkottil AP, Gebhard C, Kaufmann PA, Buechel RR. Potential of Radiation Dose Reduction by Optimizing Z-Axis Coverage in Coronary Computed Tomography Angiography on a Latest-Generation 256-Slice Scanner. J Comput Assist Tomogr. 2020 Mar/Apr;44(2):289-294. doi: 10.1097/RCT.0000000000000993.</citation>
    <PMID>32195809</PMID>
  </reference>
  <verification_date>September 2020</verification_date>
  <study_first_submitted>June 6, 2020</study_first_submitted>
  <study_first_submitted_qc>June 6, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 9, 2020</study_first_posted>
  <last_update_submitted>September 10, 2020</last_update_submitted>
  <last_update_submitted_qc>September 10, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">September 14, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Aristotle University Of Thessaloniki</investigator_affiliation>
    <investigator_full_name>Georgios P Rampidis, MD, MSc</investigator_full_name>
    <investigator_title>Academic Scholar</investigator_title>
  </responsible_party>
  <keyword>Coronary Computed Tomography Angiography</keyword>
  <keyword>Coronary Artery Calcium Score</keyword>
  <keyword>ECG Stress Test</keyword>
  <keyword>Metabolomics</keyword>
  <keyword>Precision Medicine</keyword>
  <keyword>Artificial Intelligence</keyword>
  <keyword>Machine Learning</keyword>
  <keyword>Cost-Effectiveness Analysis</keyword>
  <keyword>Clinical Decision Support Tool</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Coronary Artery Disease</mesh_term>
    <mesh_term>Myocardial Ischemia</mesh_term>
    <mesh_term>Coronary Disease</mesh_term>
    <mesh_term>Atherosclerosis</mesh_term>
    <mesh_term>Angina, Stable</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04424121
org study id: DATASET PRECISE_01062020_03448
nct id: NCT04424121
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: Open-label, 3-arm parallel randomized study
primary purpose: Diagnostic
masking: None (Open Label)
masking description: Open-label, 3-arm parallel randomized study
measure: Major Adverse Cardiac Events (MACE)
time frame: 18 months
description: Defined as cardiac death, non-fatal myocardial infarction or revascularization with percutaneous coronary intervention or coronary artery bypass graft surgery. Revascularization procedures within 6 weeks after the index CCTA will be excluded because they may be triggered by the CCTA findings per se
measure: Chest-pain rehospitalization
time frame: 18 months
description: Frequency (%) of chest-pain rehospitalization
measure: Frequency of angina [Seattle Angina Questionnaire (SAQ)]
time frame: 6 months
description: The SAQ quantifies patients' physical limitations caused by angina, the frequency of and recent changes in their symptoms, their satisfaction with treatment, and the degree to which they perceive their disease to affect their quality of life. Scores range from 0 to 100, where higher scores indicate better function (less physical limitation, less angina and better quality of life)
measure: Frequency of angina [Seattle Angina Questionnaire (SAQ)]
time frame: 12 months
description: The SAQ quantifies patients' physical limitations caused by angina, the frequency of and recent changes in their symptoms, their satisfaction with treatment, and the degree to which they perceive their disease to affect their quality of life. Scores range from 0 to 100, where higher scores indicate better function (less physical limitation, less angina and better quality of life)
measure: Frequency of angina [Seattle Angina Questionnaire (SAQ)]
time frame: 18 months
description: The SAQ quantifies patients' physical limitations caused by angina, the frequency of and recent changes in their symptoms, their satisfaction with treatment, and the degree to which they perceive their disease to affect their quality of life. Scores range from 0 to 100, where higher scores indicate better function (less physical limitation, less angina and better quality of life)
measure: Generic health status [Medical Outcomes Study 12-Item Short Form (SF-12)]
time frame: 12 months
description: The Medical Outcomes Study 12-Item Short Form (SF-12) is a general health questionnaire and is computed using the scores of 12 questions ranging from 0 to 100, where 0 indicates the lowest level of health and 100 indicates the highest level of health
arm group label: Standard of care plus ECG Stress Testing
arm group type: No Intervention
description: Participants will be approached and randomized to receive standard care plus ECG-stress testing
arm group label: Standard of care plus ECG Stress Testing and CACS
arm group type: No Intervention
description: Participants will be approached and randomized to receive standard care plus ECG-stress testing and coronary artery calcium scoring
arm group label: Standard of care plus CCTA
arm group type: Active Comparator
description: Participants will be approached and randomized to receive standard care plus ≥ 64 multidetector coronary computed tomography angiography
intervention type: Diagnostic Test
intervention name: CCTA
description: Coronary Computed Tomography Angiography
arm group label: Standard of care plus CCTA
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Haralambos Karvounis, Prof. in Cardiology
role: Study Chair
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Georgios Giannakoulas, Prof. in Cardiology
role: Study Director
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Periklis Kounatiadis, MD, PhD
role: Principal Investigator
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Panagiotis Bamidis, Prof. in Bioinformatics
role: Principal Investigator
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Georgios Rampidis, MD, MSc
role: Principal Investigator
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Olga Deda, PhD
role: Principal Investigator
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Antonios Billis, PhD
role: Principal Investigator
affiliation: Aristotle University Of Thessaloniki, School of Medicine
last name: Georgios Rampidis, MD, MSc
phone: 2310994830
phone ext: +30
email: grampidi@auth.gr
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country: Greece
citation: Benz DC, Benetos G, Rampidis G, von Felten E, Bakula A, Sustar A, Kudura K, Messerli M, Fuchs TA, Gebhard C, Pazhenkottil AP, Kaufmann PA, Buechel RR. Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy. J Cardiovasc Comput Tomogr. 2020 Sep-Oct;14(5):444-451. doi: 10.1016/j.jcct.2020.01.002. Epub 2020 Jan 13.
PMID: 31974008
citation: Benetos G, Buechel RR, Goncalves M, Benz DC, von Felten E, Rampidis GP, Clerc OF, Messerli M, Giannopoulos AA, Gebhard C, Fuchs TA, Pazhenkottil AP, Kaufmann PA, Grani C. Coronary artery volume index: a novel CCTA-derived predictor for cardiovascular events. Int J Cardiovasc Imaging. 2020 Apr;36(4):713-722. doi: 10.1007/s10554-019-01750-2. Epub 2020 Jan 1.
PMID: 31894527
citation: Rampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini Score calculation. Atherosclerosis. 2019 Aug;287:181-183. doi: 10.1016/j.atherosclerosis.2019.05.012. Epub 2019 May 10. No abstract available.
PMID: 31104809
citation: Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T, Agewall S, Dickstein K, Edvardsen T, Escaned J, Gersh BJ, Svitil P, Gilard M, Hasdai D, Hatala R, Mahfoud F, Masip J, Muneretto C, Valgimigli M, Achenbach S, Bax JJ; ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020 Jan 14;41(3):407-477. doi: 10.1093/eurheartj/ehz425. No abstract available. Erratum In: Eur Heart J. 2020 Nov 21;41(44):4242.
PMID: 31504439
citation: Budoff MJ, Mayrhofer T, Ferencik M, Bittner D, Lee KL, Lu MT, Coles A, Jang J, Krishnam M, Douglas PS, Hoffmann U; PROMISE Investigators. Prognostic Value of Coronary Artery Calcium in the PROMISE Study (Prospective Multicenter Imaging Study for Evaluation of Chest Pain). Circulation. 2017 Nov 21;136(21):1993-2005. doi: 10.1161/CIRCULATIONAHA.117.030578. Epub 2017 Aug 28.
PMID: 28847895
citation: SCOT-HEART Investigators; Newby DE, Adamson PD, Berry C, Boon NA, Dweck MR, Flather M, Forbes J, Hunter A, Lewis S, MacLean S, Mills NL, Norrie J, Roditi G, Shah ASV, Timmis AD, van Beek EJR, Williams MC. Coronary CT Angiography and 5-Year Risk of Myocardial Infarction. N Engl J Med. 2018 Sep 6;379(10):924-933. doi: 10.1056/NEJMoa1805971. Epub 2018 Aug 25.
PMID: 30145934
citation: Hilvo M, Meikle PJ, Pedersen ER, Tell GS, Dhar I, Brenner H, Schottker B, Laaperi M, Kauhanen D, Koistinen KM, Jylha A, Huynh K, Mellett NA, Tonkin AM, Sullivan DR, Simes J, Nestel P, Koenig W, Rothenbacher D, Nygard O, Laaksonen R. Development and validation of a ceramide- and phospholipid-based cardiovascular risk estimation score for coronary artery disease patients. Eur Heart J. 2020 Jan 14;41(3):371-380. doi: 10.1093/eurheartj/ehz387.
PMID: 31209498
citation: von Felten E, Messerli M, Giannopoulos AA, Benz DC, Schwyzer M, Benetos G, Rampidis G, Patriki D, Kamani CH, Grani C, Fuchs TA, Pazhenkottil AP, Gebhard C, Kaufmann PA, Buechel RR. Potential of Radiation Dose Reduction by Optimizing Z-Axis Coverage in Coronary Computed Tomography Angiography on a Latest-Generation 256-Slice Scanner. J Comput Assist Tomogr. 2020 Mar/Apr;44(2):289-294. doi: 10.1097/RCT.0000000000000993.
PMID: 32195809
responsible party type: Principal Investigator
investigator affiliation: Aristotle University Of Thessaloniki
investigator full name: Georgios P Rampidis, MD, MSc
investigator title: Academic Scholar
mesh term: Coronary Artery Disease
mesh term: Myocardial Ischemia
mesh term: Coronary Disease
mesh term: Atherosclerosis
mesh term: Angina, Stable
sharing ipd: No