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/NCT05992506</url>
    </required_header>
    <id_info>
      <org_study_id>IT21I0041</org_study_id>
      <nct_id>NCT05992506</nct_id>
    </id_info>
    <brief_title>Electroencephalographic Biomarker to Predict Postoperative Delirium</brief_title>
    <official_title>Electroencephalographic Biomarker to Predict the Development of Postoperative Delirium: a Protocol of an Observational Study in a Cohort of Patients From Five Centers</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University of Chile</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
      <collaborator>
        <agency>Instituto Nacional del Cancer, Chile</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Pontificia Universidad Catolica de Chile</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Hospital Base San Jose Osorno</agency>
        <agency_class>Other</agency_class>
      </collaborator>
      <collaborator>
        <agency>Clinica Santa Maria</agency>
        <agency_class>Other</agency_class>
      </collaborator>
    </sponsors>
    <source>University of Chile</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>
        Acute post-operatory cognitive dysfunction states are one of the most important complications&#xD;
        in older patients that underwent surgery. Among them postoperative delirium (POD) is the the&#xD;
        most studied. Patients who develop delirium have poorer long-term outcomes, such as longer&#xD;
        length of hospital stay, institutionalization at discharge, and even higher mortality, and&#xD;
        consequently, the human and economic costs significantly increase for the health system. Here&#xD;
        the research team will use an observational cohort, investigator blinded in five-center with&#xD;
        a primary endpoint to validate intraoperative EEG analysis as a reliable biomarker of&#xD;
        postoperative delirium.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        Acute post-operatory cognitive dysfunction states are one of the most frequent complications&#xD;
        in older patients after surgery, being POD the most important. Previous studies have shown&#xD;
        than the incidence of POD in older patients range between 10-50%. Patients who develop POD&#xD;
        have poorer long-term outcomes, such as longer length of hospital stay, institutionalization&#xD;
        at discharge, and even higher mortality. Consequently, the human and economic costs&#xD;
        associated to POD represents an important issue for health systems worldwide.&#xD;
  &#xD;
        A key element to diminish POD and its burden on healthcare is early diagnostic. Current risk&#xD;
        assessment tools are centered on clinical approaches based on cognitive tests (i.e., MoCA)&#xD;
        and/or prediction models that uses patients' clinical variables (i.e., DELPHI score). We have&#xD;
        developed a strategy that uses intraoperative EEG features as building blocks for a new POD&#xD;
        risk assessment predictive model. This system, called PEUMA, uses data obtained from 95&#xD;
        patients from a previous study (NCT04214496).&#xD;
  &#xD;
        This will be a multicenter (five-centers), observational study and its primary outcome will&#xD;
        be PEUMA's ability to predict POD.&#xD;
  &#xD;
        To calculate the sample size, the methodology described by Riley et al was used. This method&#xD;
        is specially designed for clinical prediction models. Such a tool is available online&#xD;
        (https://mvansmeden.shinyapps.io/BeyondEPV/). The parameters used were the following:&#xD;
  &#xD;
          -  Number of predictor candidates: 4&#xD;
  &#xD;
          -  Fraction of events: 0.22. 22% was used because it is the incidence of POD in the&#xD;
             analysis of the preliminary data of the first stage and these are in the reporting range&#xD;
             common worldwide.&#xD;
  &#xD;
          -  Estimation error of the classifier: 0.06. The authors suggest prediction errors small&#xD;
             when evaluating binary outcomes (Yes POD/No POD) The calculation indicates a sample size&#xD;
             of 240 patients. Considering a loss of 10% (in the preliminary results of the first&#xD;
             stage the loss was 8%), the sample size is 264 patients.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">August 7, 2023</start_date>
    <completion_date type="Anticipated">June 30, 2026</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2025</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>Prospective</time_perspective>
    </study_design_info>
    <target_duration>30 Days</target_duration>
    <primary_outcome>
      <measure>Postoperative Delirium</measure>
      <time_frame>First 3 days after surgery</time_frame>
      <description>Incidence of POD in the cohort diagnosed using the Confusion Assessment Method (CAM) twice/day</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Death</measure>
      <time_frame>30 days after surgery</time_frame>
      <description>Number of deceased patients</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Delirium Severity</measure>
      <time_frame>First 3 days after surgery</time_frame>
      <description>Delirium severity assessed by Cognitive Assessment Method - Severity (CAM-S)</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Delirium Duration</measure>
      <time_frame>First 3 days after surgery</time_frame>
      <description>Duration of delirium during the postoperative period</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Need for Mechanical Ventilation</measure>
      <time_frame>First 3 days after surgery</time_frame>
      <description>Number of patients that needed mechanical ventilation</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Reintervention</measure>
      <time_frame>First 3 days after surgery</time_frame>
      <description>Number of patients who required other unanticipated surgery after the primary intervention</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Unanticipated ICU hospitalization</measure>
      <time_frame>First 3 days after surgery</time_frame>
      <description>Number of patients that needed unanticipated intensive care unit (ICU) care</description>
    </secondary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">264</enrollment>
    <condition>Postoperative Delirium</condition>
    <arm_group>
      <arm_group_label>Patients at risk of developing POD</arm_group_label>
      <description>Patients older than 60 years&#xD;
  Scheduled for elective surgery of moderate or high risk (defined as that which requires a subsequent hospitalization of at least 3 days) under general anesthesia.&#xD;
  Signed informed consent.</description>
    </arm_group>
    <intervention>
      <intervention_type>Diagnostic Test</intervention_type>
      <intervention_name>POD risk estimation using PEUMA</intervention_name>
      <description>A software will analyze intraoperative EEG recording for the estimation of a POD Risk Index</description>
      <arm_group_label>Patients at risk of developing POD</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          Patients at risk of developing POD will be evaluated in the pre and postoperative period&#xD;
          for cognitive dysfunction and the appearance of POD. In the intraoperative period, an&#xD;
          EEG-based monitorization will be performed using a SedLine (Masimo, CA) Monitor.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Age ≥ 60 years old&#xD;
  &#xD;
            -  Scheduled for high-risk elective surgery&#xD;
  &#xD;
            -  Need for at least 3 days of hospital stay after surgery&#xD;
  &#xD;
            -  Surgery performed under general anesthesia&#xD;
  &#xD;
            -  Written informed consent for participation in the trial&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Patients with preoperative delirium or dementia&#xD;
  &#xD;
            -  Patients using neuroleptics drug during the past 6 months&#xD;
  &#xD;
            -  Patients with a history of encephalopathy, psychosis, stroke or brain trauma with&#xD;
               neurologic sequels&#xD;
  &#xD;
            -  The use of ketamine or dexmedetomidine during surgery&#xD;
  &#xD;
            -  Emergency surgery&#xD;
  &#xD;
            -  Mechanical ventilation during the 72 after surgery&#xD;
  &#xD;
            -  Analphabetism&#xD;
  &#xD;
            -  Patients who do not talk Spanish&#xD;
  &#xD;
            -  Patients included in another clinical trial&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>60 Years</minimum_age>
      <maximum_age>100 Years</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_contact>
      <last_name>Jose I Egana, MD, PhD</last_name>
      <phone>+56 2 2978 8209</phone>
      <email>jiegana@uchile.cl</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Antonello Penna, MD, PhD</last_name>
      <phone>+56 2 2978 8209</phone>
      <email>apenna@uchile.cl</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Hospital Clinico Universidad de Chile</name>
        <address>
          <city>Santiago</city>
          <zip>8380456</zip>
          <country>Chile</country>
        </address>
      </facility>
    </location>
    <location_countries>
      <country>Chile</country>
    </location_countries>
    <results_reference>
      <citation>Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available.</citation>
      <PMID>32188600</PMID>
    </results_reference>
    <results_reference>
      <citation>Gutierrez R, Egana JI, Saez I, Reyes F, Briceno C, Venegas M, Lavado I, Penna A. Intraoperative Low Alpha Power in the Electroencephalogram Is Associated With Postoperative Subsyndromal Delirium. Front Syst Neurosci. 2019 Oct 18;13:56. doi: 10.3389/fnsys.2019.00056. eCollection 2019.</citation>
      <PMID>31680886</PMID>
    </results_reference>
    <results_reference>
      <citation>Wong CK, van Munster BC, Hatseras A, Huis In 't Veld E, van Leeuwen BL, de Rooij SE, Pleijhuis RG. Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study. BMJ Open. 2022 Apr 8;12(4):e054023. doi: 10.1136/bmjopen-2021-054023.</citation>
      <PMID>35396283</PMID>
    </results_reference>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 7, 2023</study_first_submitted>
    <study_first_submitted_qc>August 7, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 15, 2023</study_first_posted>
    <last_update_submitted>August 7, 2023</last_update_submitted>
    <last_update_submitted_qc>August 7, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 15, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>EEG</keyword>
    <keyword>Postoperative Delirium</keyword>
    <keyword>Risk Assessment</keyword>
    <keyword>Anesthesia</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Delirium</mesh_term>
      <mesh_term>Emergence Delirium</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Undecided</sharing_ipd>
      <ipd_description>Individual participant data might be shared with other researchers only if participants give written consent for that.</ipd_description>
    </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/NCT05992506
  org study id: IT21I0041
  nct id: NCT05992506
  lead sponsor: 
  collaborator: 
  collaborator: 
  collaborator: 
  collaborator: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Acute post-operatory cognitive dysfunction states are one of the most important complications
        in older patients that underwent surgery. Among them postoperative delirium (POD) is the the
        most studied. Patients who develop delirium have poorer long-term outcomes, such as longer
        length of hospital stay, institutionalization at discharge, and even higher mortality, and
        consequently, the human and economic costs significantly increase for the health system. Here
        the research team will use an observational cohort, investigator blinded in five-center with
        a primary endpoint to validate intraoperative EEG analysis as a reliable biomarker of
        postoperative delirium.
  Acute post-operatory cognitive dysfunction states are one of the most frequent complications
        in older patients after surgery, being POD the most important. Previous studies have shown
        than the incidence of POD in older patients range between 10-50%. Patients who develop POD
        have poorer long-term outcomes, such as longer length of hospital stay, institutionalization
        at discharge, and even higher mortality. Consequently, the human and economic costs
        associated to POD represents an important issue for health systems worldwide.
  
        A key element to diminish POD and its burden on healthcare is early diagnostic. Current risk
        assessment tools are centered on clinical approaches based on cognitive tests (i.e., MoCA)
        and/or prediction models that uses patients' clinical variables (i.e., DELPHI score). We have
        developed a strategy that uses intraoperative EEG features as building blocks for a new POD
        risk assessment predictive model. This system, called PEUMA, uses data obtained from 95
        patients from a previous study (NCT04214496).
  
        This will be a multicenter (five-centers), observational study and its primary outcome will
        be PEUMA's ability to predict POD.
  
        To calculate the sample size, the methodology described by Riley et al was used. This method
        is specially designed for clinical prediction models. Such a tool is available online
        (https://mvansmeden.shinyapps.io/BeyondEPV/). The parameters used were the following:
  
          -  Number of predictor candidates: 4
  
          -  Fraction of events: 0.22. 22% was used because it is the incidence of POD in the
             analysis of the preliminary data of the first stage and these are in the reporting range
             common worldwide.
  
          -  Estimation error of the classifier: 0.06. The authors suggest prediction errors small
             when evaluating binary outcomes (Yes POD/No POD) The calculation indicates a sample size
             of 240 patients. Considering a loss of 10% (in the preliminary results of the first
             stage the loss was 8%), the sample size is 264 patients.
  observational model: Cohort
  time perspective: Prospective
  measure: Postoperative Delirium
  time frame: First 3 days after surgery
  description: Incidence of POD in the cohort diagnosed using the Confusion Assessment Method (CAM) twice/day
  measure: Death
  time frame: 30 days after surgery
  description: Number of deceased patients
  measure: Delirium Severity
  time frame: First 3 days after surgery
  description: Delirium severity assessed by Cognitive Assessment Method - Severity (CAM-S)
  measure: Delirium Duration
  time frame: First 3 days after surgery
  description: Duration of delirium during the postoperative period
  measure: Need for Mechanical Ventilation
  time frame: First 3 days after surgery
  description: Number of patients that needed mechanical ventilation
  measure: Reintervention
  time frame: First 3 days after surgery
  description: Number of patients who required other unanticipated surgery after the primary intervention
  measure: Unanticipated ICU hospitalization
  time frame: First 3 days after surgery
  description: Number of patients that needed unanticipated intensive care unit (ICU) care
  arm group label: Patients at risk of developing POD
  description: Patients older than 60 years
  Scheduled for elective surgery of moderate or high risk (defined as that which requires a subsequent hospitalization of at least 3 days) under general anesthesia.
  Signed informed consent.
  intervention type: Diagnostic Test
  intervention name: POD risk estimation using PEUMA
  description: A software will analyze intraoperative EEG recording for the estimation of a POD Risk Index
  arm group label: Patients at risk of developing POD
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: All
  minimum age: 60 Years
  maximum age: 100 Years
  healthy volunteers: Accepts Healthy Volunteers
  last name: Jose I Egana, MD, PhD
  phone: +56 2 2978 8209
  email: jiegana@uchile.cl
  last name: Antonello Penna, MD, PhD
  phone: +56 2 2978 8209
  email: apenna@uchile.cl
  facility: 
  country: Chile
  citation: Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368:m441. doi: 10.1136/bmj.m441. No abstract available.
  PMID: 32188600
  citation: Gutierrez R, Egana JI, Saez I, Reyes F, Briceno C, Venegas M, Lavado I, Penna A. Intraoperative Low Alpha Power in the Electroencephalogram Is Associated With Postoperative Subsyndromal Delirium. Front Syst Neurosci. 2019 Oct 18;13:56. doi: 10.3389/fnsys.2019.00056. eCollection 2019.
  PMID: 31680886
  citation: Wong CK, van Munster BC, Hatseras A, Huis In 't Veld E, van Leeuwen BL, de Rooij SE, Pleijhuis RG. Head-to-head comparison of 14 prediction models for postoperative delirium in elderly non-ICU patients: an external validation study. BMJ Open. 2022 Apr 8;12(4):e054023. doi: 10.1136/bmjopen-2021-054023.
  PMID: 35396283
  responsible party type: Sponsor
  mesh term: Delirium
  mesh term: Emergence Delirium
  sharing ipd: Undecided
  ipd description: Individual participant data might be shared with other researchers only if participants give written consent for that.