<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/NCT04399811</url>
  </required_header>
  <id_info>
    <org_study_id>B2020-057R</org_study_id>
    <nct_id>NCT04399811</nct_id>
  </id_info>
  <brief_title>Near-infrared Vision for Microcirculatory Status</brief_title>
  <acronym>NVIM</acronym>
  <official_title>Machine Learning-Based Near-infrared Vision to Evaluate the Microcirculatory of Critical Ill Patients: A Prospective Observational Study</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Shanghai Zhongshan Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Shanghai Zhongshan Hospital</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 investigators aimed to combine the image of near-infrared vision and machine learning&#xD;
      method to evaluate the microcirculatory status of critical ill patients.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The heat distribution of body is determined by the circulatory status. The investigators plan&#xD;
      to the near-infrared vision to collect heat distribution information of limbs. Then, the&#xD;
      machine learning method will be performed to recognize the subtle differences between images.&#xD;
      Due to lack of golden standard of microcirculatory status, indirect parameters (such as&#xD;
      lactate clearance, capillary refill time) and clinical outcomes will be recorded to evaluate&#xD;
      the performance of maching learning model.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Not yet recruiting</overall_status>
  <start_date type="Anticipated">May 17, 2020</start_date>
  <completion_date type="Anticipated">December 31, 2023</completion_date>
  <primary_completion_date type="Anticipated">December 31, 2023</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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Hospital mortality</measure>
    <time_frame>From date of admission to our ICU until the date of hospital discharge or date of death from any cause, whichever came first, assessed up to 2 months.</time_frame>
    <description>The rate of patients who died during hospital stay.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Lacteta clearance rate</measure>
    <time_frame>When the near-infrared image is taken for a patient, the blood gas analysis will be performed immediately to get the value of lactate. After 2-hours, another blood gas analysis will be conducted to get the second value of lactete.</time_frame>
    <description>The percent change of lactate between lactates measured in two time points. This parameter was usually used to guide resuscitation in septic shock.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Capillary refill time (CRT)</measure>
    <time_frame>When the near-infrared image is taken for a patient, the capillary refill time will be measured immediately.</time_frame>
    <description>A noninvasive parameter of peripheral perfusion status. CRT was measured by applying firm pressure to the ventral surface of the right index finger distal phalanx with a glass microscope slide. The pressure was increased until the skin was blank and then maintained for 10 seconds. The time for return of the normal skin color was registered with a chronometer, and a refill time greater than 3 seconds was defined as abnormal.(Hernández 2019.JAMA)</description>
  </secondary_outcome>
  <enrollment type="Anticipated">2000</enrollment>
  <condition>Near-infrared Vision</condition>
  <condition>Machine Learning</condition>
  <condition>Microcirculatory Status</condition>
  <intervention>
    <intervention_type>Diagnostic Test</intervention_type>
    <intervention_name>Near-infrared Vision Photograph</intervention_name>
    <description>The near-infrared image will be recorded (by a infrared camera connected to a laptop) for each enrolled patient</description>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        General critical ill patients who required evaluation of perfussion.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age≥18 years;&#xD;
&#xD;
          -  Patients who were transfered to our ICU.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Abnormalities of lower limbs arteries&#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>Zhe Luo</last_name>
    <phone>+8613916127028</phone>
    <email>luo.zhe@zs-hospital.sh.cn</email>
  </overall_contact>
  <verification_date>May 2020</verification_date>
  <study_first_submitted>May 16, 2020</study_first_submitted>
  <study_first_submitted_qc>May 19, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">May 22, 2020</study_first_posted>
  <last_update_submitted>May 19, 2020</last_update_submitted>
  <last_update_submitted_qc>May 19, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">May 22, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>Our institutional ethics commitee did nor approve to share data.</ipd_description>
  </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/NCT04399811
org study id: B2020-057R
nct id: NCT04399811
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The investigators aimed to combine the image of near-infrared vision and machine learning
      method to evaluate the microcirculatory status of critical ill patients.
The heat distribution of body is determined by the circulatory status. The investigators plan
      to the near-infrared vision to collect heat distribution information of limbs. Then, the
      machine learning method will be performed to recognize the subtle differences between images.
      Due to lack of golden standard of microcirculatory status, indirect parameters (such as
      lactate clearance, capillary refill time) and clinical outcomes will be recorded to evaluate
      the performance of maching learning model.
observational model: Cohort
time perspective: Prospective
measure: Hospital mortality
time frame: From date of admission to our ICU until the date of hospital discharge or date of death from any cause, whichever came first, assessed up to 2 months.
description: The rate of patients who died during hospital stay.
measure: Lacteta clearance rate
time frame: When the near-infrared image is taken for a patient, the blood gas analysis will be performed immediately to get the value of lactate. After 2-hours, another blood gas analysis will be conducted to get the second value of lactete.
description: The percent change of lactate between lactates measured in two time points. This parameter was usually used to guide resuscitation in septic shock.
measure: Capillary refill time (CRT)
time frame: When the near-infrared image is taken for a patient, the capillary refill time will be measured immediately.
description: A noninvasive parameter of peripheral perfusion status. CRT was measured by applying firm pressure to the ventral surface of the right index finger distal phalanx with a glass microscope slide. The pressure was increased until the skin was blank and then maintained for 10 seconds. The time for return of the normal skin color was registered with a chronometer, and a refill time greater than 3 seconds was defined as abnormal.(Hernández 2019.JAMA)
intervention type: Diagnostic Test
intervention name: Near-infrared Vision Photograph
description: The near-infrared image will be recorded (by a infrared camera connected to a laptop) for each enrolled patient
study pop: 
sampling method: Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Zhe Luo
phone: +8613916127028
email: luo.zhe@zs-hospital.sh.cn
responsible party type: Sponsor
sharing ipd: No
ipd description: Our institutional ethics commitee did nor approve to share data.