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/NCT05969951</url>
    </required_header>
    <id_info>
      <org_study_id>ShengjingH</org_study_id>
      <nct_id>NCT05969951</nct_id>
    </id_info>
    <brief_title>VTE Risk Prevention System Based on Big Data Analysis and Multimodule System</brief_title>
    <official_title>Construction of Perioperative Venous Thromboembolism Risk Prevention System Based on Big Data Analysis and Multimodule System</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Shengjing Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>Shengjing Hospital</source>
    <oversight_info>
      <has_dmc>Yes</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>
        Venous thromboembolism (VTE) is one of the most common complications in perioperative period&#xD;
        and the most common cause of postoperative death. VTE includes deep vein thrombosis (DVT) and&#xD;
        acute pulmonary thromboembolism (PTE). Since the embolus of PTE comes from the deep vein&#xD;
        thrombosis, and not all PE patients can present obvious clinical symptoms, VTE is currently&#xD;
        considered as a disease for research, prevention, diagnosis and treatment at home and abroad.&#xD;
        Therefore, we urgently need to develop a more comprehensive and reliable perioperative VTE&#xD;
        risk prevention system based on medical big data and multi-module computer in current&#xD;
        clinical practice, so as to effectively guide the prevention of DVT/PE, and thus reduce the&#xD;
        perioperative mortality.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Venous thromboembolism (VTE) is one of the most common complications in perioperative period&#xD;
        and the most common cause of postoperative death. VTE includes deep vein thrombosis (DVT) and&#xD;
        acute pulmonary thromboembolism (PTE). Since the embolus of PTE comes from the deep vein&#xD;
        thrombosis, and not all PE patients can present obvious clinical symptoms, VTE is currently&#xD;
        considered as a disease for research, prevention, diagnosis and treatment at home and abroad.&#xD;
        Therefore, we urgently need to develop a more comprehensive and reliable perioperative VTE&#xD;
        risk prevention system based on medical big data and multi-module computer in current&#xD;
        clinical practice, so as to effectively guide the prevention of DVT/PE, and thus reduce the&#xD;
        perioperative mortality.&#xD;
  &#xD;
        As early as 1991, Caprini et al. collated and published the clinical scale of VTE risk&#xD;
        assessment based on the study data of 538 patients. In 2010, Caprini et al. conducted&#xD;
        retrospective verification of this scale again among 8216 patients, thus making this scale an&#xD;
        important reference for many medical institutions to assess the risk of VTE occurrence in&#xD;
        perioperative patients, and it has been used ever since. However, over the past 30 years, the&#xD;
        disease spectrum, surgical methods, anesthesia methods and the cognition of thrombus&#xD;
        formation susceptibility of surgical patients have all undergone great changes. Perioperative&#xD;
        patients who are evaluated by Caprini scale and take preventive measures according to its&#xD;
        suggested measures will still have postoperative VTE. Therefore, in recent years, scholars&#xD;
        have published new VTE risk prediction methods, such as Kucher model, Padua model and so on.&#xD;
        However, the study cases established by the model are almost only about 1000 cases, and the&#xD;
        included research indicators are limited.&#xD;
  &#xD;
        The new technology realizes for the first time the identification, reading and integration of&#xD;
        VTE-related risk indicators under the guidance of informatization and digitalization,&#xD;
        providing technical support for big data analysis; To realize forward-looking, big data,&#xD;
        multi-variable, multi-module VTE-related risk assessment and prediction; The existing scoring&#xD;
        system should be improved to provide a more reliable and operable scoring model for&#xD;
        perioperative VTE prevention and treatment.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">April 14, 2021</start_date>
    <completion_date type="Anticipated">April 2026</completion_date>
    <primary_completion_date type="Anticipated">December 2025</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Control</observational_model>
      <time_perspective>Prospective</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>DVT incidence rate</measure>
      <time_frame>7±2days after operation</time_frame>
      <description>Incidence of perioperative deep venous thromboembolism</description>
    </primary_outcome>
    <primary_outcome>
      <measure>PE incidence rate</measure>
      <time_frame>7±2days after operation</time_frame>
      <description>Incidence of perioperative acute pulmonary thromboembolism</description>
    </primary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Anticipated">100000</enrollment>
    <condition>Recruiting</condition>
    <arm_group>
      <arm_group_label>New scoring mode group</arm_group_label>
      <description>Patients were evaluated for VTE risk in the perioperative period using a new scoring system</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Caprini group</arm_group_label>
      <description>Patients were evaluated for VTE risk in the perioperative period using Caprini scoring system</description>
    </arm_group>
    <eligibility>
      <study_pop>
        <textblock>
          All patients in the perioperative period between 10-90&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            1. perioperative patients;&#xD;
  &#xD;
            2. Age range from 10 to 90 years old, gender unlimited;&#xD;
  &#xD;
            3. Sign informed consent.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  (1) non-operative patients; (2) have developed deep vein thrombosis and/or pulmonary&#xD;
               embolism before surgery; (3) emergency operation patients; (4) Patients considered&#xD;
               unsuitable for inclusion in the study.&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>10 Years</minimum_age>
      <maximum_age>90 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Chuming Zhou, master</last_name>
      <phone>+8602496615-21111</phone>
      <email>zcm_06@163.com</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Yu Li, master</last_name>
      <phone>+8602496615-21111</phone>
      <email>sundy_liyu@163.com</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Shengjing Hospital of China Medical University</name>
        <address>
          <city>Shenyang</city>
          <state>Liaoning</state>
          <zip>110004</zip>
          <country>China</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Yu Li, master</last_name>
        <phone>+8602496615-21111</phone>
        <email>sundy_liyu@163.com</email>
      </contact>
      <contact_backup>
        <last_name>Chuming Zhou, master</last_name>
        <phone>+8602496615-21111</phone>
        <email>zcm_06@163.com</email>
      </contact_backup>
      <investigator>
        <last_name>Li Zhao, Doctor</last_name>
        <role>Principal Investigator</role>
      </investigator>
      <investigator>
        <last_name>Yu Li, Master</last_name>
        <role>Sub-Investigator</role>
      </investigator>
      <investigator>
        <last_name>Chuming Zhou, Master</last_name>
        <role>Sub-Investigator</role>
      </investigator>
    </location>
    <location_countries>
      <country>China</country>
    </location_countries>
    <reference>
      <citation>Cronin M, Dengler N, Krauss ES, Segal A, Wei N, Daly M, Mota F, Caprini JA. Completion of the Updated Caprini Risk Assessment Model (2013 Version). Clin Appl Thromb Hemost. 2019 Jan-Dec;25:1076029619838052. doi: 10.1177/1076029619838052.</citation>
      <PMID>30939900</PMID>
    </reference>
    <verification_date>April 2023</verification_date>
    <study_first_submitted>July 23, 2023</study_first_submitted>
    <study_first_submitted_qc>July 23, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 1, 2023</study_first_posted>
    <last_update_submitted>July 23, 2023</last_update_submitted>
    <last_update_submitted_qc>July 23, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 1, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Shengjing Hospital</investigator_affiliation>
      <investigator_full_name>LI ZHAO</investigator_full_name>
      <investigator_title>LI ZHAO</investigator_title>
    </responsible_party>
    <patient_data>
      <sharing_ipd>Undecided</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/NCT05969951
  org study id: ShengjingH
  nct id: NCT05969951
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  Venous thromboembolism (VTE) is one of the most common complications in perioperative period
        and the most common cause of postoperative death. VTE includes deep vein thrombosis (DVT) and
        acute pulmonary thromboembolism (PTE). Since the embolus of PTE comes from the deep vein
        thrombosis, and not all PE patients can present obvious clinical symptoms, VTE is currently
        considered as a disease for research, prevention, diagnosis and treatment at home and abroad.
        Therefore, we urgently need to develop a more comprehensive and reliable perioperative VTE
        risk prevention system based on medical big data and multi-module computer in current
        clinical practice, so as to effectively guide the prevention of DVT/PE, and thus reduce the
        perioperative mortality.
  Venous thromboembolism (VTE) is one of the most common complications in perioperative period
        and the most common cause of postoperative death. VTE includes deep vein thrombosis (DVT) and
        acute pulmonary thromboembolism (PTE). Since the embolus of PTE comes from the deep vein
        thrombosis, and not all PE patients can present obvious clinical symptoms, VTE is currently
        considered as a disease for research, prevention, diagnosis and treatment at home and abroad.
        Therefore, we urgently need to develop a more comprehensive and reliable perioperative VTE
        risk prevention system based on medical big data and multi-module computer in current
        clinical practice, so as to effectively guide the prevention of DVT/PE, and thus reduce the
        perioperative mortality.
  
        As early as 1991, Caprini et al. collated and published the clinical scale of VTE risk
        assessment based on the study data of 538 patients. In 2010, Caprini et al. conducted
        retrospective verification of this scale again among 8216 patients, thus making this scale an
        important reference for many medical institutions to assess the risk of VTE occurrence in
        perioperative patients, and it has been used ever since. However, over the past 30 years, the
        disease spectrum, surgical methods, anesthesia methods and the cognition of thrombus
        formation susceptibility of surgical patients have all undergone great changes. Perioperative
        patients who are evaluated by Caprini scale and take preventive measures according to its
        suggested measures will still have postoperative VTE. Therefore, in recent years, scholars
        have published new VTE risk prediction methods, such as Kucher model, Padua model and so on.
        However, the study cases established by the model are almost only about 1000 cases, and the
        included research indicators are limited.
  
        The new technology realizes for the first time the identification, reading and integration of
        VTE-related risk indicators under the guidance of informatization and digitalization,
        providing technical support for big data analysis; To realize forward-looking, big data,
        multi-variable, multi-module VTE-related risk assessment and prediction; The existing scoring
        system should be improved to provide a more reliable and operable scoring model for
        perioperative VTE prevention and treatment.
  observational model: Case-Control
  time perspective: Prospective
  measure: DVT incidence rate
  time frame: 7±2days after operation
  description: Incidence of perioperative deep venous thromboembolism
  measure: PE incidence rate
  time frame: 7±2days after operation
  description: Incidence of perioperative acute pulmonary thromboembolism
  arm group label: New scoring mode group
  description: Patients were evaluated for VTE risk in the perioperative period using a new scoring system
  arm group label: Caprini group
  description: Patients were evaluated for VTE risk in the perioperative period using Caprini scoring system
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 10 Years
  maximum age: 90 Years
  healthy volunteers: No
  last name: Chuming Zhou, master
  phone: +8602496615-21111
  email: zcm_06@163.com
  last name: Yu Li, master
  phone: +8602496615-21111
  email: sundy_liyu@163.com
  facility: 
  status: Recruiting
  contact: 
  contact backup: 
  investigator: 
  investigator: 
  investigator: 
  country: China
  citation: Cronin M, Dengler N, Krauss ES, Segal A, Wei N, Daly M, Mota F, Caprini JA. Completion of the Updated Caprini Risk Assessment Model (2013 Version). Clin Appl Thromb Hemost. 2019 Jan-Dec;25:1076029619838052. doi: 10.1177/1076029619838052.
  PMID: 30939900
  responsible party type: Principal Investigator
  investigator affiliation: Shengjing Hospital
  investigator full name: LI ZHAO
  investigator title: LI ZHAO
  sharing ipd: Undecided