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/NCT05993377</url>
    </required_header>
    <id_info>
      <org_study_id>PIFIISC21-36</org_study_id>
      <nct_id>NCT05993377</nct_id>
    </id_info>
    <brief_title>Prediction of Duration of Mechanical Ventilation in ARDS</brief_title>
    <acronym>PIONEER</acronym>
    <official_title>Predicting Length of Mechanical Ventilation in Moderate-to-severe Acute Respiratory Distress Syndrome Using Machine Learning</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>Dr. Negrin University Hospital</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Unity Health Toronto</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Cardiff University</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Leiden University Medical Center</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>Dr. Negrin 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>
        The investigators are planning to perform a secondary analysis of an academic dataset of&#xD;
        1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in&#xD;
        several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to&#xD;
        characterize the best early scenario during the first three days of diagnosis to predict&#xD;
        duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine&#xD;
        learning (ML) approaches.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        The acute respiratory distress syndrome (ARDS) is an important cause of morbidity, mortality,&#xD;
        and costs in intensive care units (ICUs) worldwide. Most ARDS patients require mechanical&#xD;
        ventilation (MV). Few studies have investigated the prediction of MV duration of ARDS.&#xD;
  &#xD;
        For model description and testing, the investigators will extract data from he first three&#xD;
        ICU days after diagnosis of moderate-to-severe ARDS from patients included in the&#xD;
        de-identified database, which includes 1,000 mechanically ventilated patients enrolled in&#xD;
        several observational cohorts in Spain, coordinated by the principal investigator (JV), and&#xD;
        funded by the Instituto de Salud Carlos III (ISCIII). The investigators will follow the&#xD;
        TRIPOD guidelines and machine learning techniques will be implemented [Random Forest (RF),&#xD;
        eXtreme Gradient Boosting (XGBoost), and Logistic regression analysis) for the development&#xD;
        and accuracy of prediction models. Disease progression will be tracked along those 3 ICU days&#xD;
        to assess lung severity according to Berlin criteria. For external validation, the&#xD;
        investigators will use 303 patients enrolled in a contemporary observational study&#xD;
        (NCT03145974). The investigators will evaluate the accuracy of prediction models by&#xD;
        calculation several statistics, such as sensitivity, specificity, positive predictive value,&#xD;
        negative value for each model. The investigators will select the best early prediction model&#xD;
        with data captured on the 1st, 2nd, or 3rd day.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Active, not recruiting</overall_status>
    <start_date type="Actual">August 14, 2023</start_date>
    <completion_date type="Anticipated">December 1, 2023</completion_date>
    <primary_completion_date type="Anticipated">December 1, 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>Days on mechanical ventilation</measure>
      <time_frame>from diagnosis to extubation</time_frame>
      <description>Duration of mechanical ventilation</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>ICU mortality</measure>
      <time_frame>up to 24 weeks</time_frame>
      <description>mortality in the intensive care unit</description>
    </secondary_outcome>
    <number_of_groups>2</number_of_groups>
    <enrollment type="Actual">1303</enrollment>
    <condition>Acute Respiratory Distress Syndrome</condition>
    <arm_group>
      <arm_group_label>Derivation and testing cohort</arm_group_label>
      <description>It will contain 1000 ARDS patients</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Confirmatory cohort</arm_group_label>
      <description>It will contain 303 patients (for external validation)</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .</intervention_name>
      <description>we will use robust machine learning approaches, such as Random Forest and XGBoost.</description>
      <arm_group_label>Confirmatory cohort</arm_group_label>
      <arm_group_label>Derivation and testing cohort</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          De-identified dataset including 1,303 patients with moderate/severe ARDS admitted&#xD;
          consecutively in a network of Spanish ICUs.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Berlin criteria for moderate to severe acute respiratory distress syndrome&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Postoperative patients ventilated &lt;24h&#xD;
  &#xD;
            -  brain death patients&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>100 Years</maximum_age>
    </eligibility>
    <overall_official>
      <last_name>Jesús Villar</last_name>
      <role>Principal Investigator</role>
      <affiliation>Hospital Universitario D. Negrin</affiliation>
    </overall_official>
    <location>
      <facility>
        <name>Hospital Universitario Dr. Negrin</name>
        <address>
          <city>Las Palmas De Gran Canaria</city>
          <state>Las Palmas</state>
          <zip>35019</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario Puerta de Hierro (ICU)</name>
        <address>
          <city>Majadahonda</city>
          <state>Madrid</state>
          <zip>28222</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario NS de Candelaria</name>
        <address>
          <city>Santa Cruz de Tenerife</city>
          <state>Tenerife</state>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital NS del Prado</name>
        <address>
          <city>Talavera de la Reina</city>
          <state>Toledo</state>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Complejo Hospitalario Universitario de Albacete (ICU)</name>
        <address>
          <city>Albacete</city>
          <zip>02006</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Complejo Hospitalario de Albacete</name>
        <address>
          <city>Albacete</city>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Department of Anesthesia, Hospital Clinic</name>
        <address>
          <city>Barcelona</city>
          <zip>08036</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital General de Ciudad Real (ICU)</name>
        <address>
          <city>Ciudad Real</city>
          <zip>13005</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Virgen de La Luz</name>
        <address>
          <city>Cuenca</city>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario de A Coruña (ICU)</name>
        <address>
          <city>La Coruña</city>
          <zip>15006</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Complejo Hospitalario Universitario de León</name>
        <address>
          <city>León</city>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario Ramón y Cajal (Anesthesia)</name>
        <address>
          <city>Madrid</city>
          <zip>28034</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario La Paz (ICU)</name>
        <address>
          <city>Madrid</city>
          <zip>28046</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Fundación Jiménez Díaz</name>
        <address>
          <city>Madrid</city>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario Virgen de Arrixaca (ICU)</name>
        <address>
          <city>Murcia</city>
          <zip>30120</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario Regional de Malaga Carlos Haya (ICU)</name>
        <address>
          <city>Málaga</city>
          <zip>29010</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario Carlos Haya</name>
        <address>
          <city>Málaga</city>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Universitario Río Hortega (ICU)</name>
        <address>
          <city>Valladolid</city>
          <zip>47012</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Hospital Virgen de la Concha (ICU)</name>
        <address>
          <city>Zamora</city>
          <zip>49022</zip>
          <country>Spain</country>
        </address>
      </facility>
    </location>
    <location>
      <facility>
        <name>Cardiff University</name>
        <address>
          <city>Cardiff</city>
          <country>United Kingdom</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Spain</country>
      <country>United Kingdom</country>
    </location_countries>
    <results_reference>
      <citation>Villar J, Ambros A, Mosteiro F, Martinez D, Fernandez L, Ferrando C, Carriedo D, Soler JA, Parrilla D, Hernandez M, Andaluz-Ojeda D, Anon JM, Vidal A, Gonzalez-Higueras E, Martin-Rodriguez C, Diaz-Lamas AM, Blanco J, Belda J, Diaz-Dominguez FJ, Rico-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.</citation>
      <PMID>30624279</PMID>
    </results_reference>
    <results_reference>
      <citation>Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30.</citation>
      <PMID>25682346</PMID>
    </results_reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 4, 2023</study_first_submitted>
    <study_first_submitted_qc>August 14, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 15, 2023</study_first_posted>
    <last_update_submitted>August 14, 2023</last_update_submitted>
    <last_update_submitted_qc>August 14, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>Dr. Negrin University Hospital</investigator_affiliation>
      <investigator_full_name>Jesus Villar</investigator_full_name>
      <investigator_title>principal investigator</investigator_title>
    </responsible_party>
    <keyword>mechanical ventilation</keyword>
    <keyword>intensive care unit</keyword>
    <keyword>duration of ventilation</keyword>
    <keyword>machine learning.</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Respiratory Distress Syndrome</mesh_term>
      <mesh_term>Respiratory Distress Syndrome, Newborn</mesh_term>
      <mesh_term>Acute Lung Injury</mesh_term>
      <mesh_term>Syndrome</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>No</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/NCT05993377
  org study id: PIFIISC21-36
  nct id: NCT05993377
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  The investigators are planning to perform a secondary analysis of an academic dataset of
        1,303 patients with moderate-to-severe acute respiratory distress syndrome (ARDS) included in
        several published cohorts (NCT00736892, NCT022288949, NCT02836444, NCT03145974), aimed to
        characterize the best early scenario during the first three days of diagnosis to predict
        duration of mechanical ventilation in the intensive care unit (ICU) using supervised machine
        learning (ML) approaches.
  The acute respiratory distress syndrome (ARDS) is an important cause of morbidity, mortality,
        and costs in intensive care units (ICUs) worldwide. Most ARDS patients require mechanical
        ventilation (MV). Few studies have investigated the prediction of MV duration of ARDS.
  
        For model description and testing, the investigators will extract data from he first three
        ICU days after diagnosis of moderate-to-severe ARDS from patients included in the
        de-identified database, which includes 1,000 mechanically ventilated patients enrolled in
        several observational cohorts in Spain, coordinated by the principal investigator (JV), and
        funded by the Instituto de Salud Carlos III (ISCIII). The investigators will follow the
        TRIPOD guidelines and machine learning techniques will be implemented [Random Forest (RF),
        eXtreme Gradient Boosting (XGBoost), and Logistic regression analysis) for the development
        and accuracy of prediction models. Disease progression will be tracked along those 3 ICU days
        to assess lung severity according to Berlin criteria. For external validation, the
        investigators will use 303 patients enrolled in a contemporary observational study
        (NCT03145974). The investigators will evaluate the accuracy of prediction models by
        calculation several statistics, such as sensitivity, specificity, positive predictive value,
        negative value for each model. The investigators will select the best early prediction model
        with data captured on the 1st, 2nd, or 3rd day.
  observational model: Cohort
  time perspective: Prospective
  measure: Days on mechanical ventilation
  time frame: from diagnosis to extubation
  description: Duration of mechanical ventilation
  measure: ICU mortality
  time frame: up to 24 weeks
  description: mortality in the intensive care unit
  arm group label: Derivation and testing cohort
  description: It will contain 1000 ARDS patients
  arm group label: Confirmatory cohort
  description: It will contain 303 patients (for external validation)
  intervention type: Other
  intervention name: Logistic regression Cross validation Area under the RIC curves Machine learning analysis. .
  description: we will use robust machine learning approaches, such as Random Forest and XGBoost.
  arm group label: Confirmatory cohort
  arm group label: Derivation and testing cohort
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: 100 Years
  last name: Jesús Villar
  role: Principal Investigator
  affiliation: Hospital Universitario D. Negrin
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  facility: 
  country: Spain
  country: United Kingdom
  citation: Villar J, Ambros A, Mosteiro F, Martinez D, Fernandez L, Ferrando C, Carriedo D, Soler JA, Parrilla D, Hernandez M, Andaluz-Ojeda D, Anon JM, Vidal A, Gonzalez-Higueras E, Martin-Rodriguez C, Diaz-Lamas AM, Blanco J, Belda J, Diaz-Dominguez FJ, Rico-Feijoo J, Martin-Delgado C, Romera MA, Gonzalez-Martin JM, Fernandez RL, Kacmarek RM; Spanish Initiative for Epidemiology, Stratification and Therapies of ARDS (SIESTA) Network. A Prognostic Enrichment Strategy for Selection of Patients With Acute Respiratory Distress Syndrome in Clinical Trials. Crit Care Med. 2019 Mar;47(3):377-385. doi: 10.1097/CCM.0000000000003624.
  PMID: 30624279
  citation: Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. J Crit Care. 2015 Jun;30(3):502-5. doi: 10.1016/j.jcrc.2015.01.020. Epub 2015 Jan 30.
  PMID: 25682346
  responsible party type: Principal Investigator
  investigator affiliation: Dr. Negrin University Hospital
  investigator full name: Jesus Villar
  investigator title: principal investigator
  mesh term: Respiratory Distress Syndrome
  mesh term: Respiratory Distress Syndrome, Newborn
  mesh term: Acute Lung Injury
  mesh term: Syndrome
  sharing ipd: No