xml: <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/NCT06029387</url>
    </required_header>
    <id_info>
      <org_study_id>AiPHD F/310003/01/X56</org_study_id>
      <nct_id>NCT06029387</nct_id>
    </id_info>
    <brief_title>Artificial Intelligence for Preventing Heart Disease (AiPHD): Observational, Single Center, Prospective and Retrospective Study</brief_title>
    <acronym>AiPHD</acronym>
    <official_title>Artificial Intelligence for Preventing Heart Disease: Observational, Single Center, Prospective and Retrospective Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>IRCCS San Raffaele</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>DGS</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>EBIT</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Dyrecta Lab</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>PORINI</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>IRCCS San Raffaele</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>
        Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary&#xD;
        computed tomography angiography (cCTA) is the first-line imaging test in patients with&#xD;
        suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. The&#xD;
        management of patients with non-obstructive CAD is unclear. This is due to the lack of&#xD;
        cCTA-based methods capable to assess the risk of disease progression towards developing major&#xD;
        adverse cardiovascular events (MACEs) based on the atherosclerosis characteristics of each&#xD;
        patient. A solution for prognostication in these patients is particularly appealing since it&#xD;
        could allow to identify patients who can benefit of a more aggressive medical treatment and&#xD;
        management, thus improving outcome.&#xD;
  &#xD;
        Proposed methods, which include qualitative evaluations such as the identification of adverse&#xD;
        atherosclerotic plaque characteristics or quantitative evaluations such as the quantification&#xD;
        of atherosclerotic plaque burden, may in some cases suffer of limited reproducibility between&#xD;
        operators and software. Most importantly, each single biomarker is insufficient to accurately&#xD;
        predict patient risk, hence potential synergic integration of cCTA and clinical biomarkers is&#xD;
        the key to efficiently guide the personalization of patient's management. Furthermore, the&#xD;
        few risk stratification methods that have been proposed are not designed to work on platforms&#xD;
        capable of deploying the solution to other clinical settings, promoting prospective or&#xD;
        external validation&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Background and Rationale&#xD;
  &#xD;
        In recent years, cCTA has become a crucial diagnostic tool for suspected CAD, recommended as&#xD;
        the first-line test for patients with an intermediate pre-test probability of CAD by the&#xD;
        European Society of Cardiology (ESC) guidelines [doi:10.1093/eurheartj/ehz425] and the&#xD;
        American HeartAssociation (AHA) [doi: 10.1016/j.jacc.2021.07.053] and Italian guidelines&#xD;
        [doi: 10.1007/s11547- 021-01378-0], because of the well-known high negative predictive value&#xD;
        of cCTA in ruling out obstructive CAD. However, most patients have non-obstructive CAD.&#xD;
  &#xD;
        While the management of patients with obstructive CAD is established, as it revolves around&#xD;
        further diagnostic test for ischemia evaluation or upfront coronary artery revascularization,&#xD;
        this is not the case for patients with non-obstructive CAD. However, these cohort of patients&#xD;
        still has a significant risk of developing major adverse cardiovascular events (MACEs) [doi:&#xD;
        10.1007/s11547- 021-01378-0] that could be prevented by implementing adequate medical&#xD;
        therapy. To date, many approaches have been proposed to tackle this issue. However, these&#xD;
        proposed solutions lack the ability to provide quantitative and reproducible results with a&#xD;
        sufficiently strong predictive value, are often proposed as a stand-alone solution without&#xD;
        the integration with multiple prognosticator imaging and clinical parameters, and are not&#xD;
        delivered through platforms capable of providing external validation and easy integration in&#xD;
        the clinical workflow. Among the proposed prognostic approaches, some are based on the&#xD;
        qualitative evaluation of coronary artery plaque features, such as positive remodeling, low&#xD;
        attenuation of the plaque, presence of spotty calcification, and &quot;napkin ring&quot; sign&#xD;
        [doi:10.1093/ehjci/jew200; DOI:10.1016/j.jcmg.2012.09.016; doi:&#xD;
        10.1161/CIRCIMAGING.117.006973], which is subject to significant inter-reader variability&#xD;
        [doi: 10.1016/j.jcct.2017.11.014].&#xD;
  &#xD;
        Other approaches rely on quantitative methods for evaluating atherosclerotic burden based on&#xD;
        the extent of coronary artery plaques and their characteristics, such as calcium density,&#xD;
        number of lesions, regional distribution, plaque volume, non-calcified plaque volume etc.&#xD;
        [DOI: 10.1016/j.ajpc.2022.100423].&#xD;
  &#xD;
        However, these approaches may be hampered by low reproducibility, especially among different&#xD;
        scanner vendors [10.1148/radiol.2016161670].&#xD;
  &#xD;
        Interestingly, a new research has also shown that, besides coronary artery vessel wall&#xD;
        characteristics, pericoronary adipose tissue attenuation carries significant predictive&#xD;
        value, as it reflects the state of coronary inflammation that plays a key role in the&#xD;
        development and progression of coronary atherosclerosis [10.1016/j.jcct.2021.03.005].&#xD;
  &#xD;
        All these CAD characteristics are often analyzed independently from one to another, reducing&#xD;
        their potential synergistic prognostic value and creating redundant variables that have&#xD;
        negligible effect on prognosis. We propose an AI-based analysis that can integrate all this&#xD;
        data in order to select the most important determinant of CAD progression and to discard&#xD;
        futile features, thus creating an agile and clinically valuable risk stratification&#xD;
        model.Furthermore, we plan to create a novel imaging marker of CAD with unfavorable outcome,&#xD;
        to be integrated in the AI-based model, which will be based on topological features of the&#xD;
        coronary artery tree. In fact, data on the association between coronary artery topology&#xD;
        (e.g., vessel-length, coronary artery volume index, cross-sectional area, curvature, and&#xD;
        tortuositv) and prognosis is scarce. However, it is known that vessel tortuosity influences&#xD;
        wall shear stress and leads to disruption of laminar flow, resulting in endothelial&#xD;
        dysfunction and flow alterations that may lead to atherosclerosis, eventually causing adverse&#xD;
        cardiac events [DOI: doi.org/10.3390/diagnostics12092178]. Thus, this novel biomarker may&#xD;
        carry a significant prognostic role. Based on these premises, our research aims to develop a&#xD;
        novel clinical-imaging AI-based model to identify and categorize patients at high risk of&#xD;
        disease progression and provide a more personalized management approach to improve patient&#xD;
        outcomes.&#xD;
  &#xD;
        However, besides the primary objective of creating an AI-based model for CAD risk&#xD;
        stratification, we aim to overcome some issues that currently hamper the widespread clinical&#xD;
        application of AI in cardiovascular care. In fact, it is recognized that the integration of&#xD;
        AI-based applications into the clinical workflow, which will increase usability and decrease&#xD;
        costs, is currently lacking [10.3389/fcvm.2021.818765].&#xD;
  &#xD;
        We aim to tackle these issues with the help of the industrial partners involved in this&#xD;
        project that will build a platform capable of delivering the software solution to provide&#xD;
        external validation of the algorithm.&#xD;
  &#xD;
        This platform will be characterized by state-of-the-art security measures, interoperability&#xD;
        with current clinical software, and easy-to-use interface.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">July 31, 2023</start_date>
    <completion_date type="Anticipated">March 1, 2028</completion_date>
    <primary_completion_date type="Anticipated">August 31, 2027</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Other</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>composite outcome 1</measure>
      <time_frame>36 months from CCTA examination</time_frame>
      <description>death by cardiovascular events or non-fatal myocardial infarction</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>composite outcome 2</measure>
      <time_frame>36 months from CCTA examination</time_frame>
      <description>all cause mortality, non-fatal myocardial infarction, hospitalization due to angina or angina-like symptoms and late coronary revascularization</description>
    </secondary_outcome>
    <other_outcome>
      <measure>change of atherosclerotic burden</measure>
      <time_frame>12 months from CCTA examination</time_frame>
      <description>regression, stability or increase measured as absolute and relative change of atherosclerotic burden between baseline and repeated CCTA</description>
    </other_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">3000</enrollment>
    <condition>Coronary Artery Disease</condition>
    <arm_group>
      <arm_group_label>Retrospective cohort</arm_group_label>
      <description>The retrospective cohort will include 2500 patients who underwent a clinically indicated cCTA examination for CAD evaluation.&#xD;
  Duration of enrollment: enrollment of patients via a telephone call will last 15 months starting from the beginning of the study (month 0).&#xD;
  Duration of total follow-up: no follow-up is planned. Duration of total study period: total retrospective study duration will be 30 months</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Prospective cohort</arm_group_label>
      <description>The prospective cohort will include 500 patients undergoing a clinically indicated cCTA for CAD evaluation.&#xD;
  Duration of enrollment: enrollment of patients at the time of cCTA examination will last 12 months.&#xD;
  Duration of total follow-up: each patient will be followed up for 36 months from the date of the cCTA.&#xD;
  Duration of total study period: total prospective study duration will be 54 months (last patient enrolled at month 12 + 36 months of follow-up + 6 months for data analysis).</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          Patients undergoing clinically indicated cCTA for suspected CAD&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients with cCTA performed for CAD assessment&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Refusal to participate in the study&#xD;
  &#xD;
            2. Age &lt;18 years old&#xD;
  &#xD;
            3. Presence of other cardiovascular comorbidities (e.g. severe valvulopathies;&#xD;
               non-ischemic cardiomyopathies; etc.)&#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>Antonio Esposito</last_name>
      <role>Principal Investigator</role>
      <affiliation>IRCCS San Raffaele</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Antonio Esposito</last_name>
      <phone>02 2643 6102</phone>
      <email>esposito.antonio@hsr.it</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Davide Vignale</last_name>
      <phone>02 2643 6113</phone>
      <email>vignale.davide@hsr.it</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>IRCCS San Raffaele</name>
        <address>
          <city>Milano</city>
          <zip>20132</zip>
          <country>Italy</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Antonio Esposito</last_name>
        <phone>02 2643 6102</phone>
      </contact>
    </location>
    <location_countries>
      <country>Italy</country>
    </location_countries>
    <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>
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      <citation>Writing Committee Members; Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, Blankstein R, Boyd J, Bullock-Palmer RP, Conejo T, Diercks DB, Gentile F, Greenwood JP, Hess EP, Hollenberg SM, Jaber WA, Jneid H, Joglar JA, Morrow DA, O'Connor RE, Ross MA, Shaw LJ. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Nov 30;78(22):e187-e285. doi: 10.1016/j.jacc.2021.07.053. Epub 2021 Oct 28.</citation>
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      <citation>Goeller M, Achenbach S, Herrmann N, Bittner DO, Kilian T, Dey D, Raaz-Schrauder D, Marwan M. Pericoronary adipose tissue CT attenuation and its association with serum levels of atherosclerosis-relevant inflammatory mediators, coronary calcification and major adverse cardiac events. J Cardiovasc Comput Tomogr. 2021 Sep-Oct;15(5):449-454. doi: 10.1016/j.jcct.2021.03.005. Epub 2021 Apr 3.</citation>
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    <verification_date>September 2023</verification_date>
    <study_first_submitted>September 1, 2023</study_first_submitted>
    <study_first_submitted_qc>September 1, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 8, 2023</study_first_posted>
    <last_update_submitted>September 1, 2023</last_update_submitted>
    <last_update_submitted_qc>September 1, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 8, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>IRCCS San Raffaele</investigator_affiliation>
      <investigator_full_name>Antonio Esposito</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <keyword>Coronary Computed Tomography Angiography</keyword>
    <keyword>Major adverse cardiovascular events</keyword>
    <keyword>Artificial Intelligence</keyword>
    <keyword>Proportional Hazards</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Coronary Artery Disease</mesh_term>
      <mesh_term>Heart Diseases</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06029387
  org study id: AiPHD F/310003/01/X56
  nct id: NCT06029387
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Coronary artery disease (CAD) is a leading cause of mortality in western countries. Coronary
        computed tomography angiography (cCTA) is the first-line imaging test in patients with
        suspected obstructive CAD. However, in most patients, cCTA shows non-obstructive CAD. The
        management of patients with non-obstructive CAD is unclear. This is due to the lack of
        cCTA-based methods capable to assess the risk of disease progression towards developing major
        adverse cardiovascular events (MACEs) based on the atherosclerosis characteristics of each
        patient. A solution for prognostication in these patients is particularly appealing since it
        could allow to identify patients who can benefit of a more aggressive medical treatment and
        management, thus improving outcome.
  
        Proposed methods, which include qualitative evaluations such as the identification of adverse
        atherosclerotic plaque characteristics or quantitative evaluations such as the quantification
        of atherosclerotic plaque burden, may in some cases suffer of limited reproducibility between
        operators and software. Most importantly, each single biomarker is insufficient to accurately
        predict patient risk, hence potential synergic integration of cCTA and clinical biomarkers is
        the key to efficiently guide the personalization of patient's management. Furthermore, the
        few risk stratification methods that have been proposed are not designed to work on platforms
        capable of deploying the solution to other clinical settings, promoting prospective or
        external validation
  Background and Rationale
  
        In recent years, cCTA has become a crucial diagnostic tool for suspected CAD, recommended as
        the first-line test for patients with an intermediate pre-test probability of CAD by the
        European Society of Cardiology (ESC) guidelines [doi:10.1093/eurheartj/ehz425] and the
        American HeartAssociation (AHA) [doi: 10.1016/j.jacc.2021.07.053] and Italian guidelines
        [doi: 10.1007/s11547- 021-01378-0], because of the well-known high negative predictive value
        of cCTA in ruling out obstructive CAD. However, most patients have non-obstructive CAD.
  
        While the management of patients with obstructive CAD is established, as it revolves around
        further diagnostic test for ischemia evaluation or upfront coronary artery revascularization,
        this is not the case for patients with non-obstructive CAD. However, these cohort of patients
        still has a significant risk of developing major adverse cardiovascular events (MACEs) [doi:
        10.1007/s11547- 021-01378-0] that could be prevented by implementing adequate medical
        therapy. To date, many approaches have been proposed to tackle this issue. However, these
        proposed solutions lack the ability to provide quantitative and reproducible results with a
        sufficiently strong predictive value, are often proposed as a stand-alone solution without
        the integration with multiple prognosticator imaging and clinical parameters, and are not
        delivered through platforms capable of providing external validation and easy integration in
        the clinical workflow. Among the proposed prognostic approaches, some are based on the
        qualitative evaluation of coronary artery plaque features, such as positive remodeling, low
        attenuation of the plaque, presence of spotty calcification, and "napkin ring" sign
        [doi:10.1093/ehjci/jew200; DOI:10.1016/j.jcmg.2012.09.016; doi:
        10.1161/CIRCIMAGING.117.006973], which is subject to significant inter-reader variability
        [doi: 10.1016/j.jcct.2017.11.014].
  
        Other approaches rely on quantitative methods for evaluating atherosclerotic burden based on
        the extent of coronary artery plaques and their characteristics, such as calcium density,
        number of lesions, regional distribution, plaque volume, non-calcified plaque volume etc.
        [DOI: 10.1016/j.ajpc.2022.100423].
  
        However, these approaches may be hampered by low reproducibility, especially among different
        scanner vendors [10.1148/radiol.2016161670].
  
        Interestingly, a new research has also shown that, besides coronary artery vessel wall
        characteristics, pericoronary adipose tissue attenuation carries significant predictive
        value, as it reflects the state of coronary inflammation that plays a key role in the
        development and progression of coronary atherosclerosis [10.1016/j.jcct.2021.03.005].
  
        All these CAD characteristics are often analyzed independently from one to another, reducing
        their potential synergistic prognostic value and creating redundant variables that have
        negligible effect on prognosis. We propose an AI-based analysis that can integrate all this
        data in order to select the most important determinant of CAD progression and to discard
        futile features, thus creating an agile and clinically valuable risk stratification
        model.Furthermore, we plan to create a novel imaging marker of CAD with unfavorable outcome,
        to be integrated in the AI-based model, which will be based on topological features of the
        coronary artery tree. In fact, data on the association between coronary artery topology
        (e.g., vessel-length, coronary artery volume index, cross-sectional area, curvature, and
        tortuositv) and prognosis is scarce. However, it is known that vessel tortuosity influences
        wall shear stress and leads to disruption of laminar flow, resulting in endothelial
        dysfunction and flow alterations that may lead to atherosclerosis, eventually causing adverse
        cardiac events [DOI: doi.org/10.3390/diagnostics12092178]. Thus, this novel biomarker may
        carry a significant prognostic role. Based on these premises, our research aims to develop a
        novel clinical-imaging AI-based model to identify and categorize patients at high risk of
        disease progression and provide a more personalized management approach to improve patient
        outcomes.
  
        However, besides the primary objective of creating an AI-based model for CAD risk
        stratification, we aim to overcome some issues that currently hamper the widespread clinical
        application of AI in cardiovascular care. In fact, it is recognized that the integration of
        AI-based applications into the clinical workflow, which will increase usability and decrease
        costs, is currently lacking [10.3389/fcvm.2021.818765].
  
        We aim to tackle these issues with the help of the industrial partners involved in this
        project that will build a platform capable of delivering the software solution to provide
        external validation of the algorithm.
  
        This platform will be characterized by state-of-the-art security measures, interoperability
        with current clinical software, and easy-to-use interface.
  observational model: Cohort
  time perspective: Other
  measure: composite outcome 1
  time frame: 36 months from CCTA examination
  description: death by cardiovascular events or non-fatal myocardial infarction
  measure: composite outcome 2
  time frame: 36 months from CCTA examination
  description: all cause mortality, non-fatal myocardial infarction, hospitalization due to angina or angina-like symptoms and late coronary revascularization
  measure: change of atherosclerotic burden
  time frame: 12 months from CCTA examination
  description: regression, stability or increase measured as absolute and relative change of atherosclerotic burden between baseline and repeated CCTA
  arm group label: Retrospective cohort
  description: The retrospective cohort will include 2500 patients who underwent a clinically indicated cCTA examination for CAD evaluation.
  Duration of enrollment: enrollment of patients via a telephone call will last 15 months starting from the beginning of the study (month 0).
  Duration of total follow-up: no follow-up is planned. Duration of total study period: total retrospective study duration will be 30 months
  arm group label: Prospective cohort
  description: The prospective cohort will include 500 patients undergoing a clinically indicated cCTA for CAD evaluation.
  Duration of enrollment: enrollment of patients at the time of cCTA examination will last 12 months.
  Duration of total follow-up: each patient will be followed up for 36 months from the date of the cCTA.
  Duration of total study period: total prospective study duration will be 54 months (last patient enrolled at month 12 + 36 months of follow-up + 6 months for data analysis).
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Antonio Esposito
  role: Principal Investigator
  affiliation: IRCCS San Raffaele
  last name: Antonio Esposito
  phone: 02 2643 6102
  email: esposito.antonio@hsr.it
  last name: Davide Vignale
  phone: 02 2643 6113
  email: vignale.davide@hsr.it
  facility: 
  status: Recruiting
  contact: 
  country: Italy
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  PMID: 31504439
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  PMID: 33881369
  responsible party type: Principal Investigator
  investigator affiliation: IRCCS San Raffaele
  investigator full name: Antonio Esposito
  investigator title: Professor
  mesh term: Coronary Artery Disease
  mesh term: Heart Diseases
  sharing ipd: No