<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/NCT04491513</url>
  </required_header>
  <id_info>
    <org_study_id>Amsterdam UMC</org_study_id>
    <nct_id>NCT04491513</nct_id>
  </id_info>
  <brief_title>Detection of Proximal Coronary Artery Disease in the Work-up for Transcatheter Aortic Valve Implantation Using CTA (From the DEPICT CTA Database)</brief_title>
  <acronym>DEPICT CTA</acronym>
  <official_title>Detection of Proximal Coronary Artery Disease in the Work-up for Transcatheter Aortic Valve Implantation Using CTA (From the DEPICT CTA Database)</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)</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>
      Background: Computed tomography angiography (CTA) is already used in the work-up for&#xD;
      transcatheter aortic valve implantation (TAVI) and could partially replace invasive coronary&#xD;
      angiography (CAG) to rule out proximal coronary lesions.&#xD;
&#xD;
      Objectives: To assess the diagnostic accuracy and yield of pre-TAVI CTA to detect coronary&#xD;
      lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and a&#xD;
      per-segment level.&#xD;
&#xD;
      Methods: The DEPICT CTA database consists of individual patient data of four studies that&#xD;
      analysed the diagnostic accuracy of pre-TAVI CTA to detect coronary lesions. For this&#xD;
      analysis, diagnostic accuracy was assessed in the left main and the three proximal coronary&#xD;
      segments.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Methods Objectives To assess the diagnostic accuracy and yield of pre-TAVI CTA to detect&#xD;
      coronary lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and&#xD;
      a per-segment level. The secondary objective was to perform a subgroup analysis of the&#xD;
      individual included studies and to assess the influence of atrial fibrillation and heart rate&#xD;
      on the diagnostic accuracy.&#xD;
&#xD;
      Literature search and study selection Studies were selected from our previous systematic&#xD;
      review and meta-analysis on the diagnostic accuracy of CTA for the detection of significant&#xD;
      CAD in TAVI work-up [4]. These studies were supplemented with studies that were retrieved by&#xD;
      an updated literature search in OVID MEDLINE (including Epub Ahead of Print, In-Process &amp;&#xD;
      Other Non-Indexed Citations) and OVID EMBASE from January 1, 1946 to October 01, 2019.&#xD;
      Studies were excluded if they were not reporting original data of patients who received both&#xD;
      pre-procedural multi-detector computed tomography (MDCT) and CAG for the evaluation of CAD in&#xD;
      the work-up of TAVI. The authors of 8 studies were approached to jointly perform a&#xD;
      meta-analysis of individual patient data. The authors of 4 studies could accommodate the data&#xD;
      for a per segment analysis [5-8]. The methodological quality of the included studies was&#xD;
      assessed using the modified Quality Assessment of Studies of Diagnostic Accuracy Included in&#xD;
      Systematic Reviews-2 criteria (QUADAS-2) [9].&#xD;
&#xD;
      Data collection The data sets included patient characteristics regarding age, sex, body mass&#xD;
      index (BMI) and the heart rate during CT-scan. Information about co-morbidities included the&#xD;
      presence of diabetes mellitus, atrial fibrillation, hypercholesterolemia, peripheral atrial&#xD;
      disease, hypertension, a history of smoking, presence of CAD and a history of PCI or coronary&#xD;
      artery bypass grafting. Technical CT scanner information included scanner type, number of&#xD;
      detector rows, number of slices, detector width, CT scanner rotation time, scan protocol and&#xD;
      settings (tube voltage and tube current), contrast agent type and volume, dose length&#xD;
      product, and nitroglycerine use.&#xD;
&#xD;
      The coronary segments were categorized in three categories: lesions &lt;50% DS, lesion ≥50-70%&#xD;
      DS or lesions ≥70% DS), according to the 18-segment coronary model of the Society of&#xD;
      Cardiovascular Computed Tomography or the American Heart Association modified 15-segment&#xD;
      model [10,11]. All studies used a cut-off of ≥50% diameter stenosis to determine the presence&#xD;
      of obstructive CAD. Three out of four studies also used a cut-off value of ≥70% DS [5,7,8],&#xD;
      which were used for an additional analysis of the diagnostic accuracy to detect ≥70% DS&#xD;
      proximal obstructive coronary lesions. If a segment had poor image quality or severe motion&#xD;
      artefacts, it was labelled as non-diagnostic.&#xD;
&#xD;
      Statistical analysis Data analysis was performed using the statistical software R version&#xD;
      3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were&#xD;
      presented as means with standard deviations (SD). The distribution of continuous variables&#xD;
      was tested with the Shapiro-Wilk test. Categorical variables were presented as frequencies&#xD;
      and percentages. The prevalence of proximal lesions of ≥50% DS and of ≥70% DS was computed&#xD;
      from the measurements with invasive CAG, because revascularization with PCI is only&#xD;
      considered in case of a proximal ≥70% DS lesion.(ref ESC guideline) Diagnostic accuracy of&#xD;
      pre-TAVI CTA, as compared to pre-TAVI CAG, was defined as the sensitivity, specificity,&#xD;
      positive - (PPV) and negative predictive value (NPV) and was derived from the number of true&#xD;
      positives (TP), true negatives (TN), false positives (TP) and false negatives (FN). The&#xD;
      analysis of the primary endpoint was performed on a per-patient and per-segment level.&#xD;
      Diagnostic accuracy was computed for CTA to detect obstructive proximal coronary lesions of&#xD;
      more than 50% DS and also of ≥70% DS lesions in a subgroup of patients were the data was&#xD;
      available. All non-diagnostic segments were considered as abnormal tests and were labelled as&#xD;
      if there was a lesion. For the per-patient analysis of diagnostic accuracy, patients were&#xD;
      regarded as having proximal obstructive lesions if there was a minimum of one non-diagnostic&#xD;
      segment. Diagnostic yield was defined as the number of patients that would not need an&#xD;
      invasive CAG if pre-TAVI CTA was used as a gatekeeper to rule out obstructive proximal&#xD;
      lesions and was derived from the sum of the negatives (TN + FN). For the subgroup analysis,&#xD;
      we analysed the diagnostic accuracy in the individual included studies and assessed the&#xD;
      diagnostic accuracy in the subgroups with atrial fibrillation and a heart rate &lt;70 and ≥70&#xD;
      b/min. The diagnostic accuracy in the individual studies and the subgroups was assessed on a&#xD;
      per-patient level for lesions ≥50% DS.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">October 1, 2019</start_date>
  <completion_date type="Actual">July 1, 2020</completion_date>
  <primary_completion_date type="Actual">July 1, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Other</observational_model>
    <time_perspective>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Diagnostic accuracy and yield of pre-TAVI CTA to detect coronary lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and a per-segment level.</measure>
    <time_frame>1 year</time_frame>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">1060</enrollment>
  <condition>Computed Tomography Angiography</condition>
  <condition>Coronary Artery Disease</condition>
  <condition>Coronary Angriography</condition>
  <condition>Aortic Valve Disease</condition>
  <arm_group>
    <arm_group_label>Overall cohort</arm_group_label>
    <description>Pateints that underwent both CTA and CAG in the work-up for TAVI</description>
  </arm_group>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Computed tomography angiography</intervention_name>
    <description>Computed tomography angiography for the detection of proximal coronary lesions in patients in the work-up for TAVI</description>
    <arm_group_label>Overall cohort</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients in the work-up for TAVI&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients in the work-up for TAVI&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  inability to undergo CTA&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <location>
    <facility>
      <name>Amsterdam UMC</name>
      <address>
        <city>Amsterdam</city>
        <state>Noord-Holland</state>
        <zip>1105AZ</zip>
        <country>Netherlands</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Netherlands</country>
  </location_countries>
  <reference>
    <citation>Andreini D, Pontone G, Mushtaq S, Bartorelli AL, Ballerini G, Bertella E, Segurini C, Conte E, Annoni A, Baggiano A, Formenti A, Fusini L, Tamborini G, Alamanni F, Fiorentini C, Pepi M. Diagnostic accuracy of multidetector computed tomography coronary angiography in 325 consecutive patients referred for transcatheter aortic valve replacement. Am Heart J. 2014 Sep;168(3):332-9. doi: 10.1016/j.ahj.2014.04.022. Epub 2014 Jun 9.</citation>
    <PMID>25173545</PMID>
  </reference>
  <reference>
    <citation>Opolski MP, Kim WK, Liebetrau C, Walther C, Blumenstein J, Gaede L, Kempfert J, Van Linden A, Walther T, Hamm CW, Mollmann H. Diagnostic accuracy of computed tomography angiography for the detection of coronary artery disease in patients referred for transcatheter aortic valve implantation. Clin Res Cardiol. 2015 Jun;104(6):471-80. doi: 10.1007/s00392-014-0806-z. Epub 2015 Jan 6.</citation>
    <PMID>25559245</PMID>
  </reference>
  <reference>
    <citation>Hamdan A, Wellnhofer E, Konen E, Kelle S, Goitein O, Andrada B, Raanani E, Segev A, Barbash I, Klempfner R, Goldenberg I, Guetta V. Coronary CT angiography for the detection of coronary artery stenosis in patients referred for transcatheter aortic valve replacement. J Cardiovasc Comput Tomogr. 2015 Jan-Feb;9(1):31-41. doi: 10.1016/j.jcct.2014.11.008. Epub 2014 Dec 3.</citation>
    <PMID>25576406</PMID>
  </reference>
  <reference>
    <citation>Rossi A, De Cecco CN, Kennon SRO, Zou L, Meinel FG, Toscano W, Segreto S, Achenbach S, Hausleiter J, Schoepf UJ, Pugliese F. CT angiography to evaluate coronary artery disease and revascularization requirement before trans-catheter aortic valve replacement. J Cardiovasc Comput Tomogr. 2017 Sep-Oct;11(5):338-346. doi: 10.1016/j.jcct.2017.06.001. Epub 2017 Jun 22.</citation>
    <PMID>28662835</PMID>
  </reference>
  <verification_date>July 2020</verification_date>
  <study_first_submitted>July 26, 2020</study_first_submitted>
  <study_first_submitted_qc>July 26, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 29, 2020</study_first_posted>
  <last_update_submitted>July 26, 2020</last_update_submitted>
  <last_update_submitted_qc>July 26, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">July 29, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)</investigator_affiliation>
    <investigator_full_name>J.P.S Henriques</investigator_full_name>
    <investigator_title>Prof</investigator_title>
  </responsible_party>
  <keyword>Computed tomography angiography</keyword>
  <keyword>coronary angriography</keyword>
  <keyword>Aortic Valve Disease</keyword>
  <keyword>Transcatheter aortic valve implantation</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>Aortic Valve Disease</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>Individual patient data will be made available during or after publication of initial outcome paper</ipd_description>
    <ipd_info_type>Study Protocol</ipd_info_type>
    <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
    <ipd_time_frame>data will be made available upon publication</ipd_time_frame>
  </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/NCT04491513
org study id: Amsterdam UMC
nct id: NCT04491513
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Background: Computed tomography angiography (CTA) is already used in the work-up for
      transcatheter aortic valve implantation (TAVI) and could partially replace invasive coronary
      angiography (CAG) to rule out proximal coronary lesions.

      Objectives: To assess the diagnostic accuracy and yield of pre-TAVI CTA to detect coronary
      lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and a
      per-segment level.

      Methods: The DEPICT CTA database consists of individual patient data of four studies that
      analysed the diagnostic accuracy of pre-TAVI CTA to detect coronary lesions. For this
      analysis, diagnostic accuracy was assessed in the left main and the three proximal coronary
      segments.
Methods Objectives To assess the diagnostic accuracy and yield of pre-TAVI CTA to detect
      coronary lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and
      a per-segment level. The secondary objective was to perform a subgroup analysis of the
      individual included studies and to assess the influence of atrial fibrillation and heart rate
      on the diagnostic accuracy.

      Literature search and study selection Studies were selected from our previous systematic
      review and meta-analysis on the diagnostic accuracy of CTA for the detection of significant
      CAD in TAVI work-up [4]. These studies were supplemented with studies that were retrieved by
      an updated literature search in OVID MEDLINE (including Epub Ahead of Print, In-Process &
      Other Non-Indexed Citations) and OVID EMBASE from January 1, 1946 to October 01, 2019.
      Studies were excluded if they were not reporting original data of patients who received both
      pre-procedural multi-detector computed tomography (MDCT) and CAG for the evaluation of CAD in
      the work-up of TAVI. The authors of 8 studies were approached to jointly perform a
      meta-analysis of individual patient data. The authors of 4 studies could accommodate the data
      for a per segment analysis [5-8]. The methodological quality of the included studies was
      assessed using the modified Quality Assessment of Studies of Diagnostic Accuracy Included in
      Systematic Reviews-2 criteria (QUADAS-2) [9].

      Data collection The data sets included patient characteristics regarding age, sex, body mass
      index (BMI) and the heart rate during CT-scan. Information about co-morbidities included the
      presence of diabetes mellitus, atrial fibrillation, hypercholesterolemia, peripheral atrial
      disease, hypertension, a history of smoking, presence of CAD and a history of PCI or coronary
      artery bypass grafting. Technical CT scanner information included scanner type, number of
      detector rows, number of slices, detector width, CT scanner rotation time, scan protocol and
      settings (tube voltage and tube current), contrast agent type and volume, dose length
      product, and nitroglycerine use.

      The coronary segments were categorized in three categories: lesions <50% DS, lesion ≥50-70%
      DS or lesions ≥70% DS), according to the 18-segment coronary model of the Society of
      Cardiovascular Computed Tomography or the American Heart Association modified 15-segment
      model [10,11]. All studies used a cut-off of ≥50% diameter stenosis to determine the presence
      of obstructive CAD. Three out of four studies also used a cut-off value of ≥70% DS [5,7,8],
      which were used for an additional analysis of the diagnostic accuracy to detect ≥70% DS
      proximal obstructive coronary lesions. If a segment had poor image quality or severe motion
      artefacts, it was labelled as non-diagnostic.

      Statistical analysis Data analysis was performed using the statistical software R version
      3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were
      presented as means with standard deviations (SD). The distribution of continuous variables
      was tested with the Shapiro-Wilk test. Categorical variables were presented as frequencies
      and percentages. The prevalence of proximal lesions of ≥50% DS and of ≥70% DS was computed
      from the measurements with invasive CAG, because revascularization with PCI is only
      considered in case of a proximal ≥70% DS lesion.(ref ESC guideline) Diagnostic accuracy of
      pre-TAVI CTA, as compared to pre-TAVI CAG, was defined as the sensitivity, specificity,
      positive - (PPV) and negative predictive value (NPV) and was derived from the number of true
      positives (TP), true negatives (TN), false positives (TP) and false negatives (FN). The
      analysis of the primary endpoint was performed on a per-patient and per-segment level.
      Diagnostic accuracy was computed for CTA to detect obstructive proximal coronary lesions of
      more than 50% DS and also of ≥70% DS lesions in a subgroup of patients were the data was
      available. All non-diagnostic segments were considered as abnormal tests and were labelled as
      if there was a lesion. For the per-patient analysis of diagnostic accuracy, patients were
      regarded as having proximal obstructive lesions if there was a minimum of one non-diagnostic
      segment. Diagnostic yield was defined as the number of patients that would not need an
      invasive CAG if pre-TAVI CTA was used as a gatekeeper to rule out obstructive proximal
      lesions and was derived from the sum of the negatives (TN + FN). For the subgroup analysis,
      we analysed the diagnostic accuracy in the individual included studies and assessed the
      diagnostic accuracy in the subgroups with atrial fibrillation and a heart rate <70 and ≥70
      b/min. The diagnostic accuracy in the individual studies and the subgroups was assessed on a
      per-patient level for lesions ≥50% DS.
observational model: Other
time perspective: Retrospective
measure: Diagnostic accuracy and yield of pre-TAVI CTA to detect coronary lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and a per-segment level.
time frame: 1 year
arm group label: Overall cohort
description: Pateints that underwent both CTA and CAG in the work-up for TAVI
intervention type: Diagnostic Test
intervention name: Computed tomography angiography
description: Computed tomography angiography for the detection of proximal coronary lesions in patients in the work-up for TAVI
arm group label: Overall cohort
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
facility: 
country: Netherlands
citation: Andreini D, Pontone G, Mushtaq S, Bartorelli AL, Ballerini G, Bertella E, Segurini C, Conte E, Annoni A, Baggiano A, Formenti A, Fusini L, Tamborini G, Alamanni F, Fiorentini C, Pepi M. Diagnostic accuracy of multidetector computed tomography coronary angiography in 325 consecutive patients referred for transcatheter aortic valve replacement. Am Heart J. 2014 Sep;168(3):332-9. doi: 10.1016/j.ahj.2014.04.022. Epub 2014 Jun 9.
PMID: 25173545
citation: Opolski MP, Kim WK, Liebetrau C, Walther C, Blumenstein J, Gaede L, Kempfert J, Van Linden A, Walther T, Hamm CW, Mollmann H. Diagnostic accuracy of computed tomography angiography for the detection of coronary artery disease in patients referred for transcatheter aortic valve implantation. Clin Res Cardiol. 2015 Jun;104(6):471-80. doi: 10.1007/s00392-014-0806-z. Epub 2015 Jan 6.
PMID: 25559245
citation: Hamdan A, Wellnhofer E, Konen E, Kelle S, Goitein O, Andrada B, Raanani E, Segev A, Barbash I, Klempfner R, Goldenberg I, Guetta V. Coronary CT angiography for the detection of coronary artery stenosis in patients referred for transcatheter aortic valve replacement. J Cardiovasc Comput Tomogr. 2015 Jan-Feb;9(1):31-41. doi: 10.1016/j.jcct.2014.11.008. Epub 2014 Dec 3.
PMID: 25576406
citation: Rossi A, De Cecco CN, Kennon SRO, Zou L, Meinel FG, Toscano W, Segreto S, Achenbach S, Hausleiter J, Schoepf UJ, Pugliese F. CT angiography to evaluate coronary artery disease and revascularization requirement before trans-catheter aortic valve replacement. J Cardiovasc Comput Tomogr. 2017 Sep-Oct;11(5):338-346. doi: 10.1016/j.jcct.2017.06.001. Epub 2017 Jun 22.
PMID: 28662835
responsible party type: Principal Investigator
investigator affiliation: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
investigator full name: J.P.S Henriques
investigator title: Prof
mesh term: Coronary Artery Disease
mesh term: Myocardial Ischemia
mesh term: Coronary Disease
mesh term: Aortic Valve Disease
sharing ipd: Yes
ipd description: Individual patient data will be made available during or after publication of initial outcome paper
ipd info type: Study Protocol
ipd info type: Statistical Analysis Plan (SAP)
ipd time frame: data will be made available upon publication