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/NCT06017505</url>
    </required_header>
    <id_info>
      <org_study_id>2023H0249</org_study_id>
      <nct_id>NCT06017505</nct_id>
    </id_info>
    <brief_title>Integrating eSAGE With EHR Data Using Machine Learning for the Early Detection and Monitoring of Cognitive Impairment in Individuals</brief_title>
    <official_title>Integrating the Electronic Self-administered Gerocognitive Examination (eSAGE) With Electronic Health Records (EHR) Data Using Machine Learning (ML) for the Early Detection and Monitoring of Cognitive Impairment in Individuals</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Douglas Scharre</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Ohio State University</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 goal of this observational trial is to leverage the electronic Self-Administered&#xD;
        Gerocognitive Examination (eSAGE), a variety of metadata (a set of data that describes and&#xD;
        gives information about other data) collected during eSAGE testing, electronic health records&#xD;
        (EHR) information, and advanced machine learning (ML) techniques to develop a new tool that&#xD;
        can aid in early-stage prediction of individuals with cognitive impairments.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This is a retrospective and prospective record review trial for patients who are followed at&#xD;
        the Center for Cognitive and Memory Disorders.&#xD;
  &#xD;
        eSAGE assessment data (including cognitive data, behavioral data, timing data and other&#xD;
        metadata) as well as varying amount of electronic health records (EHR) data will be collected&#xD;
        on all eligible subjects. Machine learning techniques with feature selection will identify&#xD;
        important EHR variables to determine what may be useful for the prediction of cognitive&#xD;
        impairment.&#xD;
  &#xD;
        Based on the EHR analysis additional questions will be added to the eSAGE to make an enhanced&#xD;
        eSAGE version (eSAGE+). The goal of the eSAGE+ is to facilitate the identification of&#xD;
        cognition impairment, and ultimately have a translational impact on Alzheimer's disease (AD)&#xD;
        identification and management.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 2023</start_date>
    <completion_date type="Anticipated">September 2025</completion_date>
    <primary_completion_date type="Anticipated">September 2025</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Other</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Area Under the Curve (AUC) for the ROC analysis in predicting subjects with cognitive impairment from cognitively normal subjects.</measure>
      <time_frame>1 day visit</time_frame>
      <description>AUC ranges in value from 0 to 1</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">1486</enrollment>
    <condition>Dementia</condition>
    <condition>Alzheimer Disease</condition>
    <condition>Mild Cognitive Impairment</condition>
    <condition>Worried Well</condition>
    <arm_group>
      <arm_group_label>Subject population</arm_group_label>
      <description>Males and females 50 years of age and over who complete the eSAGE as part of their office visit at the Center for Cognitive and Memory Disorders.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>electronic self administered gerocognitive examination (eSAGE)</intervention_name>
      <description>A self-administered digital assessment that evaluates multiple cognitive domains: orientation, language, memory, executive function, calculations, abstraction, and visuospatial abilities, through multiple questions. Additionally, it includes the collection of six clinical variables: education, gender, race, family history of dementia, stroke, and emotion.</description>
      <arm_group_label>Subject population</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          This study will include males and females 50 years of age and over who complete the eSAGE&#xD;
          as part of their office visit at the Center for Cognitive and Memory Disorders.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  1. Males and females 50 years of age and over who complete the eSAGE as part of their&#xD;
               office visit at the Center for Cognitive and Memory Disorders.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  None&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>50 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Douglas Scharre</last_name>
      <role>Principal Investigator</role>
      <affiliation>Ohio State University</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Nicole Vrettos</last_name>
      <phone>6143668642</phone>
      <email>nicole.vrettos@osumc.edu</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Jennifer Icenhour</last_name>
      <phone>6142936882</phone>
      <email>jennifer.icenhour@osumc.edu</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Nicole Vrettos</name>
        <address>
          <city>Columbus</city>
          <state>Ohio</state>
          <zip>43123</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Nicole Vrettos</last_name>
        <phone>614-366-8642</phone>
        <email>nicole.vrettos@osumc.edu</email>
      </contact>
      <contact_backup>
        <last_name>Jennifer Icenhour</last_name>
        <phone>6142936882</phone>
        <email>jennifer.icenhour@osumc.edu</email>
      </contact_backup>
      <investigator>
        <last_name>Douglas Scharre</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 15, 2023</study_first_submitted>
    <study_first_submitted_qc>August 24, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 30, 2023</study_first_posted>
    <last_update_submitted>August 24, 2023</last_update_submitted>
    <last_update_submitted_qc>August 24, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 30, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor-Investigator</responsible_party_type>
      <investigator_affiliation>Ohio State University</investigator_affiliation>
      <investigator_full_name>Douglas Scharre</investigator_full_name>
      <investigator_title>Professor-Clinical</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Alzheimer Disease</mesh_term>
      <mesh_term>Cognitive Dysfunction</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/NCT06017505
  org study id: 2023H0249
  nct id: NCT06017505
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this observational trial is to leverage the electronic Self-Administered
        Gerocognitive Examination (eSAGE), a variety of metadata (a set of data that describes and
        gives information about other data) collected during eSAGE testing, electronic health records
        (EHR) information, and advanced machine learning (ML) techniques to develop a new tool that
        can aid in early-stage prediction of individuals with cognitive impairments.
  This is a retrospective and prospective record review trial for patients who are followed at
        the Center for Cognitive and Memory Disorders.
  
        eSAGE assessment data (including cognitive data, behavioral data, timing data and other
        metadata) as well as varying amount of electronic health records (EHR) data will be collected
        on all eligible subjects. Machine learning techniques with feature selection will identify
        important EHR variables to determine what may be useful for the prediction of cognitive
        impairment.
  
        Based on the EHR analysis additional questions will be added to the eSAGE to make an enhanced
        eSAGE version (eSAGE+). The goal of the eSAGE+ is to facilitate the identification of
        cognition impairment, and ultimately have a translational impact on Alzheimer's disease (AD)
        identification and management.
  observational model: Case-Only
  time perspective: Other
  measure: Area Under the Curve (AUC) for the ROC analysis in predicting subjects with cognitive impairment from cognitively normal subjects.
  time frame: 1 day visit
  description: AUC ranges in value from 0 to 1
  arm group label: Subject population
  description: Males and females 50 years of age and over who complete the eSAGE as part of their office visit at the Center for Cognitive and Memory Disorders.
  intervention type: Diagnostic Test
  intervention name: electronic self administered gerocognitive examination (eSAGE)
  description: A self-administered digital assessment that evaluates multiple cognitive domains: orientation, language, memory, executive function, calculations, abstraction, and visuospatial abilities, through multiple questions. Additionally, it includes the collection of six clinical variables: education, gender, race, family history of dementia, stroke, and emotion.
  arm group label: Subject population
  study pop: 
  sampling method: Probability Sample
  criteria: 
  gender: All
  minimum age: 50 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Douglas Scharre
  role: Principal Investigator
  affiliation: Ohio State University
  last name: Nicole Vrettos
  phone: 6143668642
  email: nicole.vrettos@osumc.edu
  last name: Jennifer Icenhour
  phone: 6142936882
  email: jennifer.icenhour@osumc.edu
  facility: 
  contact: 
  contact backup: 
  investigator: 
  country: United States
  responsible party type: Sponsor-Investigator
  investigator affiliation: Ohio State University
  investigator full name: Douglas Scharre
  investigator title: Professor-Clinical
  mesh term: Alzheimer Disease
  mesh term: Cognitive Dysfunction
  sharing ipd: No