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/NCT06034639</url>
    </required_header>
    <id_info>
      <org_study_id>GPCRCLM0001</org_study_id>
      <nct_id>NCT06034639</nct_id>
    </id_info>
    <brief_title>AI Based Multi-modal Parameter of Peripheral Blood Cells (MMPBC) Predicts Survival Risk in Critically Ill Children</brief_title>
    <official_title>AI Based Multi-modal Parameter of Peripheral Blood Cells (MMPBC) Predicts Survival Risk in Critically Ill Children: a Multicenter, Retrospective Cohort Study</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Zhujiang Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Zhujiang Hospital</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>
        This study aims to investigate whether an AI prediction model based on blood cell multi-modal&#xD;
        data can achieve early warning of survival risk in critically ill children through a&#xD;
        large-scale multi-center cohort of critically ill children.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        According to the definition of the United Nations Children's Fund (UNICEF), children are&#xD;
        individuals between the ages of 0 and 18. Critically ill children are those who are admitted&#xD;
        to the PICU or NICU and suffer from severe illnesses that require special treatment. These&#xD;
        illnesses may endanger the child's life. Studies have reported that the international PICU&#xD;
        mortality rate in developed countries is 2% to 3%; in recent years, the in-hospital mortality&#xD;
        rate of PICU in China is 4.7% to 6.8%. The assessment of the survival risk of critically ill&#xD;
        children has always been a focus of attention. Traditional assessment methods include&#xD;
        physiological indicators, scoring tools, severity of illness, and diagnosis time, which can&#xD;
        help doctors make decisions to a certain extent, but their predictive ability is limited and&#xD;
        difficult to comprehensively reflect the child's physiological status and disease&#xD;
        progression.&#xD;
  &#xD;
        With the development of technology and social progress, blood cell analysis is evolving&#xD;
        towards a highly integrated platform of multiple cell analysis technologies that provide more&#xD;
        accurate results, more comprehensive parameters, and faster detection. Cell analysis&#xD;
        applications are increasingly focused on the identification and alarm capabilities of&#xD;
        abnormal samples, including reticulocytes, nucleated red blood cells, and immature&#xD;
        granulocytes. In 2009, Mindray Group, in collaboration with the National Key Laboratory of&#xD;
        Fine Chemicals, developed a new nucleic acid-targeted fluorescent dye that meets the&#xD;
        requirements of blood cell analysis (the patented fluorescent dye won the National Science&#xD;
        and Technology Progress Second Prize). This breakthrough technology overcame international&#xD;
        intellectual property barriers and developed the first high-end blood cell analyzer, the&#xD;
        BC-6800, with functions to detect nucleated red blood cells and reticulocytes. The device has&#xD;
        been successfully promoted to over 90% of tertiary hospitals in China. While detecting&#xD;
        routine blood cell ratios, this blood cell analyzer actually generates a large amount of&#xD;
        multi-modal data on cell distribution characteristics, including cell distribution width and&#xD;
        abnormal cell ratios. However, so far, these multi-modal data have not been fully utilized in&#xD;
        clinical practice.&#xD;
  &#xD;
        Preliminary exploration of multi-modal cell data has demonstrated its enormous value in&#xD;
        predicting, diagnosing, and prognosing infectious diseases in small populations. This study&#xD;
        aims to retrospectively collect clinical data and blood cell multi-modal data from NICU and&#xD;
        PICU hospitalized children in multiple centers across China, to establish a national&#xD;
        multi-center blood cell multi-modal database with no less than 100,000 people, and to use&#xD;
        artificial intelligence technology to achieve accurate, repeatable, and unbiased&#xD;
        identification and classification based on differences in cell morphology and structural&#xD;
        distribution. A high-performance prediction model will be constructed in the discovery cohort&#xD;
        to predict the survival risk of critically ill children; the performance of the model will be&#xD;
        validated in the validation cohort to evaluate its applicability in the Chinese population of&#xD;
        critically ill children. This study will provide solid evidence for evidence-based medicine&#xD;
        based on multi-center cohort studies and offer potential new inspection technologies for&#xD;
        predicting the survival risk of critically ill children, providing auxiliary decision support&#xD;
        for clinicians, improving the survival rate of critically ill children, and promoting the&#xD;
        development of precision medicine.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">March 1, 2023</start_date>
    <completion_date type="Anticipated">September 30, 2023</completion_date>
    <primary_completion_date type="Anticipated">September 30, 2023</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>Retrospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>death</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </primary_outcome>
    <primary_outcome>
      <measure>multiple organ dysfunction syndrome(MODS)</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </primary_outcome>
    <primary_outcome>
      <measure>sepsis</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>disseminated intravascular coagulation(DIC)</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>chronic lung disease or acute respiratory distress syndrome</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>shock</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Length of stay in the pediatric intensive care unit(PICU) or neonatal intensive care unit(NICU) hospitalization duration</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>brain injury or neurological complications</measure>
      <time_frame>through study completion, an average of 1 month</time_frame>
      <description>diagnosis time based on medical records</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">3</enrollment>
    <condition>Children</condition>
    <arm_group>
      <arm_group_label>Group 1</arm_group_label>
      <description>No Intervention</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          The included population in this study consists of children who meet the following criteria:&#xD;
          they were admitted to NICU and PICU between January 1, 2018, and March 31, 2023, and are&#xD;
          under the age of 18 with no gender limitation. These children underwent blood routine tests&#xD;
          using Mindray Medical's five-category blood cell analyzer, and the instrument or computer&#xD;
          system retained relatively complete blood cell multi-modal data. Additionally, they have&#xD;
          detailed clinical medical records related to this study. Excluded from the study are&#xD;
          children with underlying blood, genetic, or chromosomal diseases, those who have received&#xD;
          blood products in the past six months, and non-critically ill children who temporarily&#xD;
          stayed in NICU or PICU due to bed availability issues in other wards.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. Children who were admitted to NICU and PICU from January 1, 2018, to March 31, 2023.&#xD;
  &#xD;
            2. Age &lt;18 years, gender not limited.&#xD;
  &#xD;
            3. Blood routine tests were performed using Mindray Medical's five-category blood cell&#xD;
               analyzer (including BC6000, BC6000PLUS, BC6800PLUS, and BC7500 series), and the&#xD;
               instrument or computer system retained relatively complete blood cell multi-modal&#xD;
               data.&#xD;
  &#xD;
            4. Detailed clinical medical records related to this study can be obtained.&#xD;
  &#xD;
            5. Patients who were repeatedly admitted to NICU or PICU and had different conditions,&#xD;
               causes, and outcomes each time were counted as new cases.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            1. Children with congenital immunodeficiency.&#xD;
  &#xD;
            2. Children with blood diseases, including iron-deficiency anemia, macrocytic anemia,&#xD;
               hereditary spherocytosis, glucose-6-phosphate dehydrogenase deficiency, thalassemia,&#xD;
               autoimmune hemolytic anemia, aplastic anemia, immune thrombocytopenia, acute&#xD;
               lymphoblastic leukemia, acute non-lymphoblastic leukemia, multiple myeloma, allergic&#xD;
               purpura, myelodysplastic syndrome, etc.&#xD;
  &#xD;
            3. Children with genetic metabolic diseases, including galactosemia,&#xD;
               mucopolysaccharidosis, glycogen storage disease, phenylketonuria, albinism,&#xD;
               alkaptonuria, hypoxanthine-guanine phosphoribosyltransferase deficiency,&#xD;
               chromhidrosis, Goucher disease, Tay-Sachs disease, etc.&#xD;
  &#xD;
            4. Children with chromosomal diseases, including Down syndrome, trisomy 18, etc.&#xD;
  &#xD;
            5. Children who received blood products within six months, including transfused blood&#xD;
               components, human immunoglobulin, etc.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>1 Day</minimum_age>
      <maximum_age>18 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Ruowen He</last_name>
      <phone>13434240706</phone>
      <email>1577576652@qq.com</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Jielin Huang</last_name>
      <phone>13686562550</phone>
      <email>HJielin2022@163.com</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Zhujiang Hospital of Southern Medical University</name>
        <address>
          <city>Guangzhou</city>
          <state>Guangdong</state>
          <zip>510000</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Hongwei Zhou, Professor</last_name>
        <phone>18688489622</phone>
        <email>hzhou@smu.edu.cn</email>
      </contact>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <verification_date>September 2023</verification_date>
    <study_first_submitted>August 11, 2023</study_first_submitted>
    <study_first_submitted_qc>September 12, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 13, 2023</study_first_posted>
    <last_update_submitted>September 12, 2023</last_update_submitted>
    <last_update_submitted_qc>September 12, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 13, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Zhujiang Hospital</investigator_affiliation>
      <investigator_full_name>Hongwei Zhou</investigator_full_name>
      <investigator_title>high professional title</investigator_title>
    </responsible_party>
    <keyword>artificial Intelligence</keyword>
    <keyword>critically ill children</keyword>
    <keyword>survival Risk</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Critical Illness</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/NCT06034639
  org study id: GPCRCLM0001
  nct id: NCT06034639
  lead sponsor: 
  is fda regulated drug: No
  is fda regulated device: No
  This study aims to investigate whether an AI prediction model based on blood cell multi-modal
        data can achieve early warning of survival risk in critically ill children through a
        large-scale multi-center cohort of critically ill children.
  According to the definition of the United Nations Children's Fund (UNICEF), children are
        individuals between the ages of 0 and 18. Critically ill children are those who are admitted
        to the PICU or NICU and suffer from severe illnesses that require special treatment. These
        illnesses may endanger the child's life. Studies have reported that the international PICU
        mortality rate in developed countries is 2% to 3%; in recent years, the in-hospital mortality
        rate of PICU in China is 4.7% to 6.8%. The assessment of the survival risk of critically ill
        children has always been a focus of attention. Traditional assessment methods include
        physiological indicators, scoring tools, severity of illness, and diagnosis time, which can
        help doctors make decisions to a certain extent, but their predictive ability is limited and
        difficult to comprehensively reflect the child's physiological status and disease
        progression.
  
        With the development of technology and social progress, blood cell analysis is evolving
        towards a highly integrated platform of multiple cell analysis technologies that provide more
        accurate results, more comprehensive parameters, and faster detection. Cell analysis
        applications are increasingly focused on the identification and alarm capabilities of
        abnormal samples, including reticulocytes, nucleated red blood cells, and immature
        granulocytes. In 2009, Mindray Group, in collaboration with the National Key Laboratory of
        Fine Chemicals, developed a new nucleic acid-targeted fluorescent dye that meets the
        requirements of blood cell analysis (the patented fluorescent dye won the National Science
        and Technology Progress Second Prize). This breakthrough technology overcame international
        intellectual property barriers and developed the first high-end blood cell analyzer, the
        BC-6800, with functions to detect nucleated red blood cells and reticulocytes. The device has
        been successfully promoted to over 90% of tertiary hospitals in China. While detecting
        routine blood cell ratios, this blood cell analyzer actually generates a large amount of
        multi-modal data on cell distribution characteristics, including cell distribution width and
        abnormal cell ratios. However, so far, these multi-modal data have not been fully utilized in
        clinical practice.
  
        Preliminary exploration of multi-modal cell data has demonstrated its enormous value in
        predicting, diagnosing, and prognosing infectious diseases in small populations. This study
        aims to retrospectively collect clinical data and blood cell multi-modal data from NICU and
        PICU hospitalized children in multiple centers across China, to establish a national
        multi-center blood cell multi-modal database with no less than 100,000 people, and to use
        artificial intelligence technology to achieve accurate, repeatable, and unbiased
        identification and classification based on differences in cell morphology and structural
        distribution. A high-performance prediction model will be constructed in the discovery cohort
        to predict the survival risk of critically ill children; the performance of the model will be
        validated in the validation cohort to evaluate its applicability in the Chinese population of
        critically ill children. This study will provide solid evidence for evidence-based medicine
        based on multi-center cohort studies and offer potential new inspection technologies for
        predicting the survival risk of critically ill children, providing auxiliary decision support
        for clinicians, improving the survival rate of critically ill children, and promoting the
        development of precision medicine.
  observational model: Case-Only
  time perspective: Retrospective
  measure: death
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: multiple organ dysfunction syndrome(MODS)
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: sepsis
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: disseminated intravascular coagulation(DIC)
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: chronic lung disease or acute respiratory distress syndrome
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: shock
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: Length of stay in the pediatric intensive care unit(PICU) or neonatal intensive care unit(NICU) hospitalization duration
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  measure: brain injury or neurological complications
  time frame: through study completion, an average of 1 month
  description: diagnosis time based on medical records
  arm group label: Group 1
  description: No Intervention
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 1 Day
  maximum age: 18 Years
  healthy volunteers: No
  last name: Ruowen He
  phone: 13434240706
  email: 1577576652@qq.com
  last name: Jielin Huang
  phone: 13686562550
  email: HJielin2022@163.com
  facility: 
  status: Recruiting
  contact: 
  country: China
  responsible party type: Principal Investigator
  investigator affiliation: Zhujiang Hospital
  investigator full name: Hongwei Zhou
  investigator title: high professional title
  mesh term: Critical Illness