<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/NCT01700465</url>
  </required_header>
  <id_info>
    <org_study_id>11-1437</org_study_id>
    <nct_id>NCT01700465</nct_id>
  </id_info>
  <brief_title>Estimating and Predicting Hemodynamic Changes During Hemodialysis</brief_title>
  <official_title>Estimating and Predicting Hemodynamic Changes During Hemodialysis</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) estimate&#xD;
      acute blood loss volume, 2) monitor and estimate fluid resuscitation needs, 3) predict&#xD;
      cardiovascular collapse well ahead of any clinically significant changes in standard vital&#xD;
      signs, and 4) estimate intracranial pressure. We hypothesize that these same methods can be&#xD;
      used to monitor volume loss during hemodialysis, as well as predict intradialytic&#xD;
      hypotension, well before it occurs.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      1. Collect physiological waveform data from patients undergoing hemodialysis at the&#xD;
           University of Colorado Hospital, Children's Hospital Colorado, and Fresenius Medical&#xD;
           Centers using non-invasive monitoring techniques.&#xD;
&#xD;
        2. Combine the physiological data from patient monitors with clinical and demographic data,&#xD;
           including age, gender, race, problem list, reason for dialysis, estimated dry weight,&#xD;
           volume removed, arterial and venous pressures, etc. for use in developing mathematical&#xD;
           models of hemodialysis.&#xD;
&#xD;
        3. Develop robust, real-time, computational models for:&#xD;
&#xD;
             -  estimating acute intravascular volume loss during hemodialysis&#xD;
&#xD;
             -  predicting an optimal, individual specific, intravascular volume to be removed&#xD;
                during a hemodialysis session&#xD;
&#xD;
             -  predicting intradialytic hypotension&#xD;
&#xD;
        4. Determine:&#xD;
&#xD;
             -  which non-invasive signals are relevant to each model type&#xD;
&#xD;
             -  which features extracted from these signals are relevant&#xD;
&#xD;
             -  which algorithms are capable of using the extracted features for each decision type&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date>September 2012</start_date>
  <completion_date type="Actual">December 2016</completion_date>
  <primary_completion_date type="Actual">December 2016</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>Acute intravascular volume loss during hemodialysis</measure>
    <time_frame>one hemodialysis session (approx 3-4 hours)</time_frame>
    <description>development of algorithm to estimate acute intravascular volume loss during hemodialysis</description>
  </primary_outcome>
  <number_of_groups>1</number_of_groups>
  <enrollment type="Actual">241</enrollment>
  <condition>Hemodialysis</condition>
  <arm_group>
    <arm_group_label>Hemodialysis</arm_group_label>
    <description>Patients undergoing hemodialysis</description>
  </arm_group>
  <eligibility>
    <study_pop>
      <textblock>
        Adult and pediatric patients undergoing hemodialysis at Fresenius Medical Centers,&#xD;
        University of Colorado Hospital or Children's Hospital Colorado will be the population base&#xD;
        for enrollment in this study. Patients may have acute kidney injury or end stage renal&#xD;
        disease. Subjects may be inpatients or outpatients.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Age: 2 - 89 years&#xD;
&#xD;
          -  Undergoing hemodialysis at the Fresenius Medical Centers, University of Colorado&#xD;
             Hospital or Children's Hospital Colorado&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Pregnant&#xD;
&#xD;
          -  Incarcerated&#xD;
&#xD;
          -  Decisionally challenged&#xD;
&#xD;
          -  Positive for hepatitis B surface antigen&#xD;
&#xD;
          -  Limited access to or compromised monitoring sites for non-invasive finger and ear or&#xD;
             forehead sensors&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>2 Years</minimum_age>
    <maximum_age>89 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Steve Moulton, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Children's Hospital Colorado</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Fresenius Medical Center East Denver</name>
      <address>
        <city>Aurora</city>
        <state>Colorado</state>
        <zip>80011</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <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>
    <facility>
      <name>Fresenius Medical Center Central</name>
      <address>
        <city>Denver</city>
        <state>Colorado</state>
        <zip>80209</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Fresenius Medical Center Rocky Mountain</name>
      <address>
        <city>Denver</city>
        <state>Colorado</state>
        <zip>80220</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>December 2016</verification_date>
  <study_first_submitted>September 27, 2012</study_first_submitted>
  <study_first_submitted_qc>October 3, 2012</study_first_submitted_qc>
  <study_first_posted type="Estimate">October 4, 2012</study_first_posted>
  <last_update_submitted>December 1, 2016</last_update_submitted>
  <last_update_submitted_qc>December 1, 2016</last_update_submitted_qc>
  <last_update_posted type="Estimate">December 5, 2016</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Hemodialysis</keyword>
  <keyword>Hemodynamics</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/NCT01700465
org study id: 11-1437
nct id: NCT01700465
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) estimate
      acute blood loss volume, 2) monitor and estimate fluid resuscitation needs, 3) predict
      cardiovascular collapse well ahead of any clinically significant changes in standard vital
      signs, and 4) estimate intracranial pressure. We hypothesize that these same methods can be
      used to monitor volume loss during hemodialysis, as well as predict intradialytic
      hypotension, well before it occurs.
1. Collect physiological waveform data from patients undergoing hemodialysis at the
           University of Colorado Hospital, Children's Hospital Colorado, and Fresenius Medical
           Centers using non-invasive monitoring techniques.

        2. Combine the physiological data from patient monitors with clinical and demographic data,
           including age, gender, race, problem list, reason for dialysis, estimated dry weight,
           volume removed, arterial and venous pressures, etc. for use in developing mathematical
           models of hemodialysis.

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

             -  estimating acute intravascular volume loss during hemodialysis

             -  predicting an optimal, individual specific, intravascular volume to be removed
                during a hemodialysis session

             -  predicting intradialytic hypotension

        4. Determine:

             -  which non-invasive signals are relevant to each model type

             -  which features extracted from these signals are relevant

             -  which algorithms are capable of using the extracted features for each decision type
observational model: Case-Only
time perspective: Prospective
measure: Acute intravascular volume loss during hemodialysis
time frame: one hemodialysis session (approx 3-4 hours)
description: development of algorithm to estimate acute intravascular volume loss during hemodialysis
arm group label: Hemodialysis
description: Patients undergoing hemodialysis
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 2 Years
maximum age: 89 Years
healthy volunteers: No
last name: Steve Moulton, MD
role: Principal Investigator
affiliation: Children's Hospital Colorado
facility: 
facility: 
facility: 
facility: 
facility: 
country: United States
responsible party type: Sponsor