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/NCT05986474</url>
    </required_header>
    <id_info>
      <org_study_id>H-2102-192-1203</org_study_id>
      <nct_id>NCT05986474</nct_id>
    </id_info>
    <brief_title>Development of Synthetic Medical Data Generation Technology to Predict Postoperative Complications</brief_title>
    <official_title>Development of Synthetic Medical Data Generation Technology to Predict Postoperative Complications</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Seoul National University Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Seoul National University 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>
        &lt;Development of synthetic medical data generation technology to predict postoperative&#xD;
        complications&gt;&#xD;
  &#xD;
        In order to develop a model for predicting the occurrence of complications after surgery, it&#xD;
        is necessary to establish a cohort along with statistical indicators related to the&#xD;
        occurrence of complications. This study aims to combine synthetic medical data based on&#xD;
        actual clinical data and develop a predictive model based on synthetic medical data.&#xD;
  &#xD;
        This will allow researchers to conduct research only with synthetic data without dealing with&#xD;
        actual medical data, allowing them to use and process data without legal constraints, and to&#xD;
        create as much data as they want based on various preprocessed, standardized, and labeled raw&#xD;
        data.&#xD;
  &#xD;
        Patients from three hospitals in Korea (Seoul National University Hospital, Seoul National&#xD;
        University Bundang Hospital, Seoul Metropolitan City-Boramae Medical Center) were enrolled&#xD;
        for the study.&#xD;
  &#xD;
        Medical data (both clinical and laboratory) from 410,000 patients who were conducted surgery&#xD;
        between 2005 and 2020 were collected to evaluate the performance of the prediction model&#xD;
        using AKI-based prediction model development and external verification.&#xD;
  &#xD;
        Based on the collected patient data, synthetic medical data were combined using the machine&#xD;
        learning algorithm, and the anonymity and re-identification of the synthesized medical data&#xD;
        were evaluated.&#xD;
  &#xD;
        Also, the development of AI-based prediction model using synthetic medical data and the&#xD;
        actual medical data model were compared.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Completed</overall_status>
    <start_date type="Actual">March 26, 2021</start_date>
    <completion_date type="Actual">March 25, 2023</completion_date>
    <primary_completion_date type="Actual">September 27, 2022</primary_completion_date>
    <study_type>Observational [Patient Registry]</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Cohort</observational_model>
      <time_perspective>Cross-Sectional</time_perspective>
    </study_design_info>
    <target_duration>5 Years</target_duration>
    <primary_outcome>
      <measure>Acute kidney injury</measure>
      <time_frame>After surgery, within 7 days</time_frame>
      <description>Surgical complication 1</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Acute kidney disease</measure>
      <time_frame>After surgery, within 3 months</time_frame>
      <description>Surgical complication 2</description>
    </primary_outcome>
    <enrollment type="Actual">410000</enrollment>
    <condition>Acute Kidney Injury</condition>
    <condition>Surgery-Complications</condition>
    <condition>Nephropathy</condition>
    <eligibility>
      <study_pop>
        <textblock>
          Patients over 18 years who underwent non-cardiothoracic and non-vascular surgery&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Patients over 18 years.&#xD;
  &#xD;
            -  Non-cardiothoracic and non-vascular surgery from five departments (general surgery,&#xD;
               obstetrics and gynecology (OBGY), urologic surgery, neurosurgery, and orthopedic&#xD;
               surgery)&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  1) no information of baseline (≤90 days before surgery) or follow-up (≤7 days after&#xD;
               surgery) renal function&#xD;
  &#xD;
            -  2) exclusive surgery, including surgeries of deceased patients or surgery that&#xD;
               directly affect renal function (partial or total nephrectomy, kidney transplantation)&#xD;
  &#xD;
            -  3) preoperative advanced kidney dysfunction, including preoperative serum creatinine&#xD;
               (SCr) ≥4.0 mg/dL, baseline eGFR (estimated glomerular filtration rate) &lt;15 mL/min/1.73&#xD;
               m2, preoperative kidney replacement therapy history, or AKI history within 2 weeks of&#xD;
               surgery&#xD;
  &#xD;
            -  4) surgery other than general or spinal anesthesia (local anesthesia or monitored&#xD;
               anesthesia care)&#xD;
  &#xD;
            -  5) missing covariates.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <location>
      <facility>
        <name>Seoul National University Hospital</name>
        <address>
          <city>Seoul</city>
          <country>Korea, Republic of</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Korea, Republic of</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 3, 2023</study_first_submitted>
    <study_first_submitted_qc>August 3, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 14, 2023</study_first_posted>
    <last_update_submitted>August 3, 2023</last_update_submitted>
    <last_update_submitted_qc>August 3, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Seoul National University Hospital</investigator_affiliation>
      <investigator_full_name>Hajeong Lee</investigator_full_name>
      <investigator_title>Associate Professor</investigator_title>
    </responsible_party>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Acute Kidney Injury</mesh_term>
      <mesh_term>Postoperative Complications</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/NCT05986474
  org study id: H-2102-192-1203
  nct id: NCT05986474
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  <Development of synthetic medical data generation technology to predict postoperative
        complications>
  
        In order to develop a model for predicting the occurrence of complications after surgery, it
        is necessary to establish a cohort along with statistical indicators related to the
        occurrence of complications. This study aims to combine synthetic medical data based on
        actual clinical data and develop a predictive model based on synthetic medical data.
  
        This will allow researchers to conduct research only with synthetic data without dealing with
        actual medical data, allowing them to use and process data without legal constraints, and to
        create as much data as they want based on various preprocessed, standardized, and labeled raw
        data.
  
        Patients from three hospitals in Korea (Seoul National University Hospital, Seoul National
        University Bundang Hospital, Seoul Metropolitan City-Boramae Medical Center) were enrolled
        for the study.
  
        Medical data (both clinical and laboratory) from 410,000 patients who were conducted surgery
        between 2005 and 2020 were collected to evaluate the performance of the prediction model
        using AKI-based prediction model development and external verification.
  
        Based on the collected patient data, synthetic medical data were combined using the machine
        learning algorithm, and the anonymity and re-identification of the synthesized medical data
        were evaluated.
  
        Also, the development of AI-based prediction model using synthetic medical data and the
        actual medical data model were compared.
  observational model: Cohort
  time perspective: Cross-Sectional
  measure: Acute kidney injury
  time frame: After surgery, within 7 days
  description: Surgical complication 1
  measure: Acute kidney disease
  time frame: After surgery, within 3 months
  description: Surgical complication 2
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: No
  facility: 
  country: Korea, Republic of
  responsible party type: Principal Investigator
  investigator affiliation: Seoul National University Hospital
  investigator full name: Hajeong Lee
  investigator title: Associate Professor
  mesh term: Acute Kidney Injury
  mesh term: Postoperative Complications