<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/NCT03769948</url>
  </required_header>
  <id_info>
    <org_study_id>HR-18-087</org_study_id>
    <nct_id>NCT03769948</nct_id>
  </id_info>
  <brief_title>Clinical Decision Support Algorithm to Predict Diabetic Retinopathy</brief_title>
  <official_title>Validating a Clinical Decision Support Algorithm Developed With Demographic, Co-morbidity, and Lab Data to Diagnose, Stage, Prevent, and Monitor a Patient's Diabetic Retinopathy</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Oklahoma State University Center for Health Sciences</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Oklahoma State University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>University of Oklahoma</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Oklahoma State University Center for Health Sciences</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Diabetic retinopathy (DR), a complication of diabetes, is a leading cause of blindness among&#xD;
      working-aged adults globally. In its early stages, DR is symptomless, and can only be&#xD;
      detected by an annual eye exam. Once the disease has progressed to the point where vision&#xD;
      loss has occurred, the damage is irreversible. Consequently, early detection is&#xD;
      quintessential in treating DR. Two barriers to early detection are poor patient compliance&#xD;
      with the annual exam and lack of access to specialists in rural areas. This research is&#xD;
      focused on developing and validating new, cost-effective predictive technologies that can&#xD;
      improve early screening of DR. Our overall objective is to develop and implement an entire&#xD;
      suite of tools to detect diabetes complications in order to augment care for underserved&#xD;
      rural populations in the US and internationally.&#xD;
    </textblock>
  </brief_summary>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Not yet recruiting</last_known_status>
  <start_date type="Anticipated">February 2022</start_date>
  <completion_date type="Anticipated">July 2023</completion_date>
  <primary_completion_date type="Anticipated">August 2022</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>Retrospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Diabetic retinopathy indicator (yes/no)</measure>
    <time_frame>March, 2019</time_frame>
    <description>Diabetic patients with 362.0x ICD-9 codes are classified as DR patient</description>
  </primary_outcome>
  <enrollment type="Anticipated">500</enrollment>
  <condition>Diabetic Retinopathy</condition>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>risk factors for diabetic retinopathy</intervention_name>
    <description>Demographic variables: gender, race, marital status, urban rural status. Co-morbidity variable: neuropathy, nephropathy, peripheral circulatory, ketoacidosis, hyperosmolarity Lab tests variables: alanine aminotransferase (ALT), albumin serum, anion gap, aspartate aminotransferase, blood urea nitrogen, calcium serum, chloride serum, creatinine serum, glucose serum plasma, hematocrit, hemoglobin, mean corpuscular hemoglobin concentration (MCHC), mean platelet volume (MPV), potassium serum, protein total serum, red blood cell (RBC) count, sodium serum, white blood cell (WBC) count</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        The cohort will be selected from diabetic patients that have Electronic Medical Records in&#xD;
        Harold Hamm Diabetes Center.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
        For diabetic patients with DR:&#xD;
&#xD;
          -  With 250.xx diabetes and 362.0x DR ICD-9 codes&#xD;
&#xD;
          -  All variables are complete within the observation window&#xD;
&#xD;
        For diabetic patients without DR:&#xD;
&#xD;
          -  With 250.xx diabetes ICD-9 codes&#xD;
&#xD;
          -  Without 362.0x DR ICD-9 codes&#xD;
&#xD;
          -  All variables are complete within the observation window&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
  </eligibility>
  <verification_date>September 2020</verification_date>
  <study_first_submitted>December 6, 2018</study_first_submitted>
  <study_first_submitted_qc>December 6, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 10, 2018</study_first_posted>
  <last_update_submitted>September 17, 2020</last_update_submitted>
  <last_update_submitted_qc>September 17, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">September 18, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Oklahoma State University</investigator_affiliation>
    <investigator_full_name>William Paiva</investigator_full_name>
    <investigator_title>Executive Director</investigator_title>
  </responsible_party>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Retinal Diseases</mesh_term>
    <mesh_term>Diabetic Retinopathy</mesh_term>
  </condition_browse>
  <!-- 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/NCT03769948
org study id: HR-18-087
nct id: NCT03769948
lead sponsor: 
collaborator: 
collaborator: 
is fda regulated drug: No
is fda regulated device: No
Diabetic retinopathy (DR), a complication of diabetes, is a leading cause of blindness among
      working-aged adults globally. In its early stages, DR is symptomless, and can only be
      detected by an annual eye exam. Once the disease has progressed to the point where vision
      loss has occurred, the damage is irreversible. Consequently, early detection is
      quintessential in treating DR. Two barriers to early detection are poor patient compliance
      with the annual exam and lack of access to specialists in rural areas. This research is
      focused on developing and validating new, cost-effective predictive technologies that can
      improve early screening of DR. Our overall objective is to develop and implement an entire
      suite of tools to detect diabetes complications in order to augment care for underserved
      rural populations in the US and internationally.
observational model: Cohort
time perspective: Retrospective
measure: Diabetic retinopathy indicator (yes/no)
time frame: March, 2019
description: Diabetic patients with 362.0x ICD-9 codes are classified as DR patient
intervention type: Other
intervention name: risk factors for diabetic retinopathy
description: Demographic variables: gender, race, marital status, urban rural status. Co-morbidity variable: neuropathy, nephropathy, peripheral circulatory, ketoacidosis, hyperosmolarity Lab tests variables: alanine aminotransferase (ALT), albumin serum, anion gap, aspartate aminotransferase, blood urea nitrogen, calcium serum, chloride serum, creatinine serum, glucose serum plasma, hematocrit, hemoglobin, mean corpuscular hemoglobin concentration (MCHC), mean platelet volume (MPV), potassium serum, protein total serum, red blood cell (RBC) count, sodium serum, white blood cell (WBC) count
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
responsible party type: Principal Investigator
investigator affiliation: Oklahoma State University
investigator full name: William Paiva
investigator title: Executive Director
mesh term: Retinal Diseases
mesh term: Diabetic Retinopathy