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/NCT05992324</url>
    </required_header>
    <id_info>
      <org_study_id>LungPivotal</org_study_id>
      <nct_id>NCT05992324</nct_id>
    </id_info>
    <brief_title>A Validation Study to Evaluate the Performance of Caption Health Lung Guidance and Interpretation</brief_title>
    <official_title>A Validation Study to Evaluate the Performance of Caption Health Lung Guidance and Interpretation</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Caption Health, Inc.</agency>
        <agency_class>Industry</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Caption Health, Inc.</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>Yes</is_fda_regulated_device>
      <is_unapproved_device>Yes</is_unapproved_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        The purpose of this study is to assess the efficacy of Caption LungAI.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        After being informed about the study, all patients giving written informed consent will&#xD;
        undergo two 8-zone protocol lung ultrasound exams. One exam will be conducted by an expert&#xD;
        lung ultrasound user without Caption LungAI and one exam conducted by a (non-expert)&#xD;
        healthcare provider who is trained on Caption LungAI and will use Caption LungAI to capture&#xD;
        images.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">July 17, 2023</start_date>
    <completion_date type="Anticipated">January 1, 2024</completion_date>
    <primary_completion_date type="Anticipated">January 1, 2024</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>N/A</allocation>
      <intervention_model>Single Group Assignment</intervention_model>
      <primary_purpose>Diagnostic</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Diagnostic Image Quality (Trained Healthcare Professional)</measure>
      <time_frame>Up to 24 weeks from completion of the study.</time_frame>
      <description>Evaluate the product's ability to assist the user to capture a LUS image with diagnostic quality. Percentage of zones with diagnostic image quality &gt; 0.80</description>
    </primary_outcome>
    <primary_outcome>
      <measure>B-Lines Detection</measure>
      <time_frame>Up to 24 weeks from completion of the study.</time_frame>
      <description>Evaluate the product's ability to retrospectively detect present B-lines on images acquired by a local expert with no Caption Lung AI assistance. AUROC &gt; 0.80; Sensitivity (Se) &gt; 0.80; Specificity (Sp) &gt; 0.75</description>
    </primary_outcome>
    <primary_outcome>
      <measure>B-Line Significance</measure>
      <time_frame>Up to 24 weeks from completion of the study.</time_frame>
      <description>Evaluate the product's ability to retrospectively detect B-lines of significant severity on images acquired by a local expert with no Caption Lung AI assistance. AUROC &gt; 0.80; Sensitivity (Se) &gt; 0.80; Specificity (Sp) &gt; 0.75</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Remote Reader Performance</measure>
      <time_frame>Up to 24 weeks from completion of the study.</time_frame>
      <description>Evaluate the product's impact on a remote expert reader's ability to interpret LUS images. Difference Between Aided &amp;&#xD;
  Unaided Groups:&#xD;
  AUROC &gt; 0.00; Sensitivity (Se) &gt; 0.00; Specificity (Sp) &gt; 0.00</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Sub-group Analyses</measure>
      <time_frame>Up to 24 weeks from completion of the study.</time_frame>
      <description>The following sub-group analyses will be performed for the primary endpoints: age (&lt; 65, ≥ 65), BMI group (&lt; 25, 25 ≤ BMI &lt; 30, ≥ 30), gender (Male / Female), site location, trained HCP, and zone of the image (1 - 8).</description>
    </secondary_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">220</enrollment>
    <condition>Shortness of Breath</condition>
    <arm_group>
      <arm_group_label>Patients presenting with clinical suspicion of B-Lines</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>Patients will undergo two 8-zone protocol lung ultrasound exams. One exam will be conducted by an expert lung ultrasound user without Caption LungAI and will last approximately 5-10 minutes. The second exam will be conducted by a healthcare professional with Caption LungAI and will last approximately 15-20 minutes.</description>
    </arm_group>
    <intervention>
      <intervention_type>Device</intervention_type>
      <intervention_name>Caption LungAI</intervention_name>
      <description>Caption LungAI is a software that is designed to help non-expert healthcare professionals acquire diagnostic quality images on an 8-zone lung protocol for both healthy patients and patients presenting with pathology such as B-Lines.</description>
      <arm_group_label>Patients presenting with clinical suspicion of B-Lines</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients over the age of 18&#xD;
  &#xD;
            -  Patients presenting to the hospital or outpatient setting with shortness of breath and&#xD;
               suspected B-lines.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients in extremis/in whom a research lung ultrasound would not normally be&#xD;
               performed due to other priorities&#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_contact>
      <last_name>Roman Zingarelli</last_name>
      <phone>6145786384</phone>
      <email>roman@captionhealth.com</email>
    </overall_contact>
    <location>
      <facility>
        <name>Yale New Haven Hospital</name>
        <address>
          <city>New Haven</city>
          <state>Connecticut</state>
          <zip>06510</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Not yet recruiting</status>
      <contact>
        <last_name>Ryan Denkewicz</last_name>
        <email>ryan.denkewicz@yale.edu</email>
      </contact>
      <investigator>
        <last_name>Chris Moore, MD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location>
      <facility>
        <name>Northwestern University</name>
        <address>
          <city>Chicago</city>
          <state>Illinois</state>
          <zip>60611</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Not yet recruiting</status>
      <contact>
        <last_name>Sylwia Clarke</last_name>
        <phone>312-694-0802</phone>
        <email>Sylwia.Clarke@nm.org</email>
      </contact>
      <investigator>
        <last_name>Baljash Cheema, MD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location>
      <facility>
        <name>Rush University Medical Center</name>
        <address>
          <city>Chicago</city>
          <state>Illinois</state>
          <zip>60612</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Pamela Manning</last_name>
        <phone>312-942-3470</phone>
        <email>Pamela_Manning@rush.edu</email>
      </contact>
      <investigator>
        <last_name>Michael Gottlieb, MD</last_name>
        <role>Principal Investigator</role>
      </investigator>
    </location>
    <location>
      <facility>
        <name>The Moses H. Cone Memorial Hospital</name>
        <address>
          <city>Greensboro</city>
          <state>North Carolina</state>
          <zip>27401</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Not yet recruiting</status>
      <contact>
        <last_name>Jennifer Knapp</last_name>
        <email>Jennifer.Knapp@conehealth.com</email>
      </contact>
      <investigator>
        <last_name>Ravi Agarwala, MD</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>July 31, 2023</study_first_submitted>
    <study_first_submitted_qc>August 7, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 15, 2023</study_first_posted>
    <last_update_submitted>August 7, 2023</last_update_submitted>
    <last_update_submitted_qc>August 7, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>Lung Ultrasound</keyword>
    <keyword>B-Lines</keyword>
    <keyword>POCUS by non-expert user</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Dyspnea</mesh_term>
    </condition_browse>
    <!-- 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/NCT05992324
  org study id: LungPivotal
  nct id: NCT05992324
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: Yes
  is unapproved device: Yes
  The purpose of this study is to assess the efficacy of Caption LungAI.
  After being informed about the study, all patients giving written informed consent will
        undergo two 8-zone protocol lung ultrasound exams. One exam will be conducted by an expert
        lung ultrasound user without Caption LungAI and one exam conducted by a (non-expert)
        healthcare provider who is trained on Caption LungAI and will use Caption LungAI to capture
        images.
  allocation: N/A
  intervention model: Single Group Assignment
  primary purpose: Diagnostic
  masking: None (Open Label)
  measure: Diagnostic Image Quality (Trained Healthcare Professional)
  time frame: Up to 24 weeks from completion of the study.
  description: Evaluate the product's ability to assist the user to capture a LUS image with diagnostic quality. Percentage of zones with diagnostic image quality > 0.80
  measure: B-Lines Detection
  time frame: Up to 24 weeks from completion of the study.
  description: Evaluate the product's ability to retrospectively detect present B-lines on images acquired by a local expert with no Caption Lung AI assistance. AUROC > 0.80; Sensitivity (Se) > 0.80; Specificity (Sp) > 0.75
  measure: B-Line Significance
  time frame: Up to 24 weeks from completion of the study.
  description: Evaluate the product's ability to retrospectively detect B-lines of significant severity on images acquired by a local expert with no Caption Lung AI assistance. AUROC > 0.80; Sensitivity (Se) > 0.80; Specificity (Sp) > 0.75
  measure: Remote Reader Performance
  time frame: Up to 24 weeks from completion of the study.
  description: Evaluate the product's impact on a remote expert reader's ability to interpret LUS images. Difference Between Aided &
  Unaided Groups:
  AUROC > 0.00; Sensitivity (Se) > 0.00; Specificity (Sp) > 0.00
  measure: Sub-group Analyses
  time frame: Up to 24 weeks from completion of the study.
  description: The following sub-group analyses will be performed for the primary endpoints: age (< 65, ≥ 65), BMI group (< 25, 25 ≤ BMI < 30, ≥ 30), gender (Male / Female), site location, trained HCP, and zone of the image (1 - 8).
  arm group label: Patients presenting with clinical suspicion of B-Lines
  arm group type: Experimental
  description: Patients will undergo two 8-zone protocol lung ultrasound exams. One exam will be conducted by an expert lung ultrasound user without Caption LungAI and will last approximately 5-10 minutes. The second exam will be conducted by a healthcare professional with Caption LungAI and will last approximately 15-20 minutes.
  intervention type: Device
  intervention name: Caption LungAI
  description: Caption LungAI is a software that is designed to help non-expert healthcare professionals acquire diagnostic quality images on an 8-zone lung protocol for both healthy patients and patients presenting with pathology such as B-Lines.
  arm group label: Patients presenting with clinical suspicion of B-Lines
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  last name: Roman Zingarelli
  phone: 6145786384
  email: roman@captionhealth.com
  facility: 
  status: Not yet recruiting
  contact: 
  investigator: 
  facility: 
  status: Not yet recruiting
  contact: 
  investigator: 
  facility: 
  status: Recruiting
  contact: 
  investigator: 
  facility: 
  status: Not yet recruiting
  contact: 
  investigator: 
  country: United States
  responsible party type: Sponsor
  mesh term: Dyspnea