<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/NCT01699243</url>
  </required_header>
  <id_info>
    <org_study_id>12-0990</org_study_id>
    <nct_id>NCT01699243</nct_id>
  </id_info>
  <brief_title>Continuous Noninvasive Method for Estimating and Predicting Maternal and Fetal Hemodynamic Changes During Regional Anesthesia</brief_title>
  <official_title>A Continuous, Noninvasive Method for Estimating and Predicting Maternal and Fetal Hemodynamic Changes During Regional Anesthesia</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Colorado, Denver</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Colorado, Denver</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      Machine learning techniques and algorithms originally developed for use in the field of&#xD;
      robotics can be applied to continuous, noninvasive physiological waveform data to discover&#xD;
      hidden, hemodynamic relationships. Newly developed algorithms can, in real-time: 1) predict&#xD;
      cardiovascular collapse well ahead of any clinically significant changes in standard vital&#xD;
      signs, 2) monitor and estimate fluid resuscitation needs, 3) estimate acute blood loss&#xD;
      volume, and 4) estimate intracranial pressure. The investigators hypothesize that these same&#xD;
      methods can be used to predict functional hypovolemia during regional anesthesia for labor or&#xD;
      fetal intervention.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Specific aims:&#xD;
&#xD;
        1. Collect noninvasive physiological waveform data from patients undergoing regional&#xD;
           anesthesia for labor or fetal intervention at the University of Colorado Hospital and&#xD;
           Children's Hospital Colorado.&#xD;
&#xD;
        2. Combine the physiological data from patient monitors with clinical and demographic data,&#xD;
           including maternal problem list, medications, volume infused, use of vasopressors,&#xD;
           arterial and venous pressures, fetal heart rate, fetal umbilical artery Doppler&#xD;
           velocimetry, maternal uterine artery Doppler waveform, fetal and neonatal outcomes etc.&#xD;
           for use in developing mathematical model for early detection of maternal functional&#xD;
           hypovolemia.&#xD;
&#xD;
        3. Develop robust, real-time, computational models for:&#xD;
&#xD;
             -  estimating maternal volume status prior to administration of epidural anesthesia&#xD;
&#xD;
             -  estimating effective intravascular volume loss during maternal regional anesthesia&#xD;
&#xD;
             -  predicting an optimal, individual specific requirement for IV resuscitation and/or&#xD;
                need for vasopressor agents while providing adequate analgesia using regional&#xD;
                techniques and optimizing the fetal outcomes&#xD;
&#xD;
             -  identifying mothers susceptible to epidural induced hypotension&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Enrolling by invitation</overall_status>
  <start_date>September 2012</start_date>
  <completion_date type="Anticipated">June 2024</completion_date>
  <primary_completion_date type="Anticipated">June 2024</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>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>effective intravascular volume loss during maternal regional anesthesia</measure>
    <time_frame>during epidural, 1-4 hours</time_frame>
    <description>Develop algorithm for estimating effective intravascular volume loss during maternal regional anesthesia</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Anticipated">19</enrollment>
  <condition>Pregnancy</condition>
  <condition>Anesthesia</condition>
  <arm_group>
    <arm_group_label>Epidural</arm_group_label>
    <description>subjects under epidural anesthesia</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        All pregnant females undergoing regional anesthesia for labor or fetal intervention at the&#xD;
        University of Colorado Hospital and Children's Hospital Colorado.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  1. Age: 14 - 44 years&#xD;
&#xD;
          -  2. Pregnant&#xD;
&#xD;
          -  3. Undergoing regional anesthesia for labor or fetal intervention at the University of&#xD;
             Colorado Hospital and Children's Hospital Colorado&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  1. Severe pre-eclampsia/eclampsia&#xD;
&#xD;
          -  2. Pre-procedural maternal hypertension requiring treatment&#xD;
&#xD;
          -  3. Significant fetal heart rate abnormalities prior to regional anesthesia&#xD;
&#xD;
          -  4. Incarcerated&#xD;
&#xD;
          -  5. Decisionally challenged&#xD;
&#xD;
          -  6. Limited access to or compromised monitoring sites for non-invasive finger and ear&#xD;
             or forehead sensors&#xD;
      </textblock>
    </criteria>
    <gender>Female</gender>
    <minimum_age>14 Years</minimum_age>
    <maximum_age>44 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Steve Moulton, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Colorado, Denver</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Children's Hospital Colorado</name>
      <address>
        <city>Aurora</city>
        <state>Colorado</state>
        <zip>80045</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>University of Colorado Hospital</name>
      <address>
        <city>Aurora</city>
        <state>Colorado</state>
        <zip>80045</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>August 2022</verification_date>
  <study_first_submitted>October 1, 2012</study_first_submitted>
  <study_first_submitted_qc>October 1, 2012</study_first_submitted_qc>
  <study_first_posted type="Estimate">October 3, 2012</study_first_posted>
  <last_update_submitted>August 16, 2022</last_update_submitted>
  <last_update_submitted_qc>August 16, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">August 17, 2022</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>pregnancy</keyword>
  <keyword>labor</keyword>
  <keyword>epidural</keyword>
  <keyword>fetal intervention</keyword>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


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/NCT01699243
org study id: 12-0990
nct id: NCT01699243
lead sponsor: 
has dmc: No
Machine learning techniques and algorithms originally developed for use in the field of
      robotics can be applied to continuous, noninvasive physiological waveform data to discover
      hidden, hemodynamic relationships. Newly developed algorithms can, in real-time: 1) predict
      cardiovascular collapse well ahead of any clinically significant changes in standard vital
      signs, 2) monitor and estimate fluid resuscitation needs, 3) estimate acute blood loss
      volume, and 4) estimate intracranial pressure. The investigators hypothesize that these same
      methods can be used to predict functional hypovolemia during regional anesthesia for labor or
      fetal intervention.
Specific aims:

        1. Collect noninvasive physiological waveform data from patients undergoing regional
           anesthesia for labor or fetal intervention at the University of Colorado Hospital and
           Children's Hospital Colorado.

        2. Combine the physiological data from patient monitors with clinical and demographic data,
           including maternal problem list, medications, volume infused, use of vasopressors,
           arterial and venous pressures, fetal heart rate, fetal umbilical artery Doppler
           velocimetry, maternal uterine artery Doppler waveform, fetal and neonatal outcomes etc.
           for use in developing mathematical model for early detection of maternal functional
           hypovolemia.

        3. Develop robust, real-time, computational models for:

             -  estimating maternal volume status prior to administration of epidural anesthesia

             -  estimating effective intravascular volume loss during maternal regional anesthesia

             -  predicting an optimal, individual specific requirement for IV resuscitation and/or
                need for vasopressor agents while providing adequate analgesia using regional
                techniques and optimizing the fetal outcomes

             -  identifying mothers susceptible to epidural induced hypotension
observational model: Case-Only
time perspective: Prospective
measure: effective intravascular volume loss during maternal regional anesthesia
time frame: during epidural, 1-4 hours
description: Develop algorithm for estimating effective intravascular volume loss during maternal regional anesthesia
arm group label: Epidural
description: subjects under epidural anesthesia
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: Female
minimum age: 14 Years
maximum age: 44 Years
healthy volunteers: No
last name: Steve Moulton, MD
role: Principal Investigator
affiliation: University of Colorado, Denver
facility: 
facility: 
country: United States
responsible party type: Sponsor