<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/NCT03235856</url>
  </required_header>
  <id_info>
    <org_study_id>ECR-AS-2017-12</org_study_id>
    <nct_id>NCT03235856</nct_id>
  </id_info>
  <brief_title>Retrospective Digital Computer Analysis of Keratoconus Evolution - REDCAKE</brief_title>
  <acronym>REDCAKE</acronym>
  <official_title>Retrospective Digital Computer Analysis of Keratoconus Evolution - REDCAKE</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University Hospital, Antwerp</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>European Vision Institute Clinical Research Network</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University Hospital, Antwerp</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 purpose of this study is to create a database of keratoconic eyes with two or more&#xD;
      corneal topographies/tomographies, at least 5 months apart&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Title: Keratometry values such as K1, K2 and the angle between these two; Value and location&#xD;
      of the thinnest corneal point; Pachymetry progression (radial change of pachymetry); IS value&#xD;
      (i.e., ratio of average curvature in superior and inferior sections)&#xD;
&#xD;
      Description:&#xD;
&#xD;
      The primary endpoint is to obtain a database, containing at least two valid corneal biometry&#xD;
      measurements (Scheimpflug) recorded at least 5 months apart, for a predetermined number of&#xD;
      suitable keratoconus patients.&#xD;
&#xD;
      These data will be used to create a personalized three-dimensional model of the cornea at&#xD;
      each time point, which permits classifying corneas according to shape and stage, as well as&#xD;
      assessing the influence of patient age, gender, family history and ophthalmic habits (e.g.&#xD;
      eye rubbing) on keratoconus progression. Based on corneal changes over time, an estimate of&#xD;
      the underlying biomechanical changes will be made. All these data will then be combined to&#xD;
      develop software for automated keratoconus detection and progression risk assessment to help&#xD;
      ophthalmologists decide when to perform crosslinking on their patients.&#xD;
&#xD;
      The primary variables are the elevation parameters derived directly from the Scheimpflug&#xD;
      measuring device export files, along with the demographic and medical information (if&#xD;
      available).&#xD;
&#xD;
      Time frame: 5 months&#xD;
&#xD;
      Once a predictive model for keratoconus progression speed based on multiple measurements,&#xD;
      this can be improved to make predictions based on a single measurement. Furthermore, the&#xD;
      database obtained in this work will also be a valuable resource to analyse the variation in&#xD;
      keratoconus shape, which may lead to an improved classification of keratoconus types&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">September 1, 2017</start_date>
  <completion_date type="Actual">December 31, 2018</completion_date>
  <primary_completion_date type="Actual">December 31, 2018</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>To obtain a database, containing at least two valid corneal Scheimpflug measurements recorded at least 5 months apart, of 1500 keratoconus patients.</measure>
    <time_frame>5 months</time_frame>
    <description>These data will be used to create a personalized three-dimensional model of the cornea at each time point, which permits classifying corneas according to shape and stage, as well as assessing the influence of patient age, gender, family history and ophthalmic habits (e.g. eye rubbing) on keratoconus progression. Based on corneal changes over time, an estimate of the underlying biomechanical changes will be made.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>To develop software to automatically detect keratoconus and estimate the keratoconus progression speed</measure>
    <time_frame>18 months</time_frame>
    <description>The data from Outcome 1 will be combined to develop software for automated keratoconus detection and progression risk assessment to help ophthalmologists decide when to perform cross-linking on their patients.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>To improve the predictive model for keratoconus progression speed so it can work using only one single measurement</measure>
    <time_frame>18 months</time_frame>
    <description>Once a predictive model for keratoconus progression speed based on multiple measurements, this can be improved to make predictions based on a single measurement.</description>
  </secondary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">972</enrollment>
  <condition>Keratoconus</condition>
  <arm_group>
    <arm_group_label>Keratoconus patients</arm_group_label>
    <description>This study involves a retrospective analysis of data recorded during routine clinical follow-up of keratoconus patients. As such, the impact for the patient is minimal as no additional tests need to be performed</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        This study involves a retrospective analysis of data recorded during routine clinical&#xD;
        follow-up of keratoconus patients. As such, the impact for the patient is minimal as no&#xD;
        additions tests need to be performed&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age between 12-40 years.&#xD;
&#xD;
          -  Clinically diagnosed mild or moderate keratoconus in one or both eyes&#xD;
&#xD;
          -  Two or more Scheimpflug measurements (type Pentacam HR, Pentacam AXL, Ziemer Galilei,&#xD;
             CSO Sirius) of good technical quality, separated at least five months apart.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Corneal scarring present in both eyes.&#xD;
&#xD;
          -  Known corneal or retinal pathologies, apart from keratoconus&#xD;
&#xD;
          -  Known ocular procedures/ treatments (including crosslinking)&#xD;
&#xD;
          -  Known systemic diseases (e.g. diabetes, MS, HIV/AIDS, hypertension,…), except&#xD;
             allergies&#xD;
&#xD;
          -  Change in contact lenses between measurements (e.g. start wearing lenses, change from&#xD;
             corneal to scleral lenses, etc.)&#xD;
&#xD;
          -  Fluorescein drops instilled into the eye before Scheimpflug measurement.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>12 Years</minimum_age>
    <maximum_age>40 Years</maximum_age>
  </eligibility>
  <location>
    <facility>
      <name>Antwerp University Hospital</name>
      <address>
        <city>Edegem</city>
        <zip>2650</zip>
        <country>Belgium</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Belgium</country>
  </location_countries>
  <verification_date>November 2020</verification_date>
  <study_first_submitted>July 27, 2017</study_first_submitted>
  <study_first_submitted_qc>July 31, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 1, 2017</study_first_posted>
  <last_update_submitted>November 2, 2020</last_update_submitted>
  <last_update_submitted_qc>November 2, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">November 3, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University Hospital, Antwerp</investigator_affiliation>
    <investigator_full_name>Jos Rozema</investigator_full_name>
    <investigator_title>Senior researcher, Dpt of Ophthalmology</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Keratoconus</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/NCT03235856
org study id: ECR-AS-2017-12
nct id: NCT03235856
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The purpose of this study is to create a database of keratoconic eyes with two or more
      corneal topographies/tomographies, at least 5 months apart
Title: Keratometry values such as K1, K2 and the angle between these two; Value and location
      of the thinnest corneal point; Pachymetry progression (radial change of pachymetry); IS value
      (i.e., ratio of average curvature in superior and inferior sections)

      Description:

      The primary endpoint is to obtain a database, containing at least two valid corneal biometry
      measurements (Scheimpflug) recorded at least 5 months apart, for a predetermined number of
      suitable keratoconus patients.

      These data will be used to create a personalized three-dimensional model of the cornea at
      each time point, which permits classifying corneas according to shape and stage, as well as
      assessing the influence of patient age, gender, family history and ophthalmic habits (e.g.
      eye rubbing) on keratoconus progression. Based on corneal changes over time, an estimate of
      the underlying biomechanical changes will be made. All these data will then be combined to
      develop software for automated keratoconus detection and progression risk assessment to help
      ophthalmologists decide when to perform crosslinking on their patients.

      The primary variables are the elevation parameters derived directly from the Scheimpflug
      measuring device export files, along with the demographic and medical information (if
      available).

      Time frame: 5 months

      Once a predictive model for keratoconus progression speed based on multiple measurements,
      this can be improved to make predictions based on a single measurement. Furthermore, the
      database obtained in this work will also be a valuable resource to analyse the variation in
      keratoconus shape, which may lead to an improved classification of keratoconus types
observational model: Other
time perspective: Retrospective
measure: To obtain a database, containing at least two valid corneal Scheimpflug measurements recorded at least 5 months apart, of 1500 keratoconus patients.
time frame: 5 months
description: These data will be used to create a personalized three-dimensional model of the cornea at each time point, which permits classifying corneas according to shape and stage, as well as assessing the influence of patient age, gender, family history and ophthalmic habits (e.g. eye rubbing) on keratoconus progression. Based on corneal changes over time, an estimate of the underlying biomechanical changes will be made.
measure: To develop software to automatically detect keratoconus and estimate the keratoconus progression speed
time frame: 18 months
description: The data from Outcome 1 will be combined to develop software for automated keratoconus detection and progression risk assessment to help ophthalmologists decide when to perform cross-linking on their patients.
measure: To improve the predictive model for keratoconus progression speed so it can work using only one single measurement
time frame: 18 months
description: Once a predictive model for keratoconus progression speed based on multiple measurements, this can be improved to make predictions based on a single measurement.
arm group label: Keratoconus patients
description: This study involves a retrospective analysis of data recorded during routine clinical follow-up of keratoconus patients. As such, the impact for the patient is minimal as no additional tests need to be performed
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 12 Years
maximum age: 40 Years
facility: 
country: Belgium
responsible party type: Principal Investigator
investigator affiliation: University Hospital, Antwerp
investigator full name: Jos Rozema
investigator title: Senior researcher, Dpt of Ophthalmology
mesh term: Keratoconus
sharing ipd: No